{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# CNN",
   "id": "339783e1cdf1695c"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-12T05:29:41.561841Z",
     "start_time": "2025-06-12T05:28:56.299971Z"
    }
   },
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "# 导入PyTorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torchvision import transforms\n",
    "\n",
    "# 设置随机种子以便结果可复现\n",
    "np.random.seed(42)\n",
    "torch.manual_seed(42)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(42)\n",
    "\n",
    "# 设置文件路径\n",
    "base_path = r\"C://Users/30979//Desktop//mnist_dataset\"\n",
    "train_path = os.path.join(base_path, \"mnist_train.csv\")\n",
    "test_path = os.path.join(base_path, \"mnist_test.csv\")\n",
    "\n",
    "# 检查是否有可用的GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"使用设备: {device}\")\n",
    "\n",
    "# 1. 加载数据\n",
    "print(\"正在加载数据...\")\n",
    "train_data = pd.read_csv(train_path)\n",
    "test_data = pd.read_csv(test_path)\n",
    "\n",
    "print(f\"训练集大小: {train_data.shape}\")\n",
    "print(f\"测试集大小: {test_data.shape}\")\n",
    "\n",
    "# 2. 数据预处理\n",
    "print(\"\\n数据预处理...\")\n",
    "# 分离特征和标签\n",
    "X_train = train_data.drop(columns=['label']).values.astype('float32')\n",
    "y_train = train_data['label'].values.astype('int64')\n",
    "X_test = test_data.drop(columns=['label']).values.astype('float32')\n",
    "y_test = test_data['label'].values.astype('int64')\n",
    "\n",
    "# 归一化像素值到0-1范围\n",
    "X_train = X_train / 255.0\n",
    "X_test = X_test / 255.0\n",
    "\n",
    "# 重塑数据为CNN所需的格式：[样本数, 通道数, 高度, 宽度]\n",
    "X_train = X_train.reshape(-1, 1, 28, 28)\n",
    "X_test = X_test.reshape(-1, 1, 28, 28)\n",
    "\n",
    "# 转换为PyTorch张量\n",
    "X_train_tensor = torch.tensor(X_train)\n",
    "y_train_tensor = torch.tensor(y_train)\n",
    "X_test_tensor = torch.tensor(X_test)\n",
    "y_test_tensor = torch.tensor(y_test)\n",
    "\n",
    "# 创建数据集和数据加载器\n",
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "test_dataset = TensorDataset(X_test_tensor, y_test_tensor)\n",
    "\n",
    "batch_size = 128\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "print(f\"处理后训练数据形状: {X_train.shape}\")\n",
    "print(f\"处理后测试数据形状: {X_test.shape}\")\n",
    "\n",
    "# 可视化一些样本\n",
    "plt.figure(figsize=(10, 5))\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    plt.imshow(X_train[i].reshape(28, 28), cmap='gray')\n",
    "    plt.title(f\"Label: {y_train[i]}\")\n",
    "    plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 3. 定义CNN模型\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        # 第一个卷积层\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(1, 32, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2)\n",
    "        )\n",
    "        \n",
    "        # 第二个卷积层\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(32, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2)\n",
    "        )\n",
    "        \n",
    "        # 第三个卷积层\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2)\n",
    "        )\n",
    "        \n",
    "        # 全连接层\n",
    "        self.fc1 = nn.Linear(128 * 3 * 3, 128)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc2 = nn.Linear(128, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = x.view(x.size(0), -1)  # 展平\n",
    "        x = self.fc1(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "# 初始化模型并移至GPU（如果可用）\n",
    "model = CNN().to(device)\n",
    "\n",
    "# 打印模型结构\n",
    "print(\"\\nCNN模型结构:\")\n",
    "print(model)\n",
    "\n",
    "# 4. 训练模型\n",
    "print(\"\\n开始训练CNN模型...\")\n",
    "# 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters())\n",
    "\n",
    "# 设置早停参数\n",
    "patience = 5\n",
    "best_val_loss = float('inf')\n",
    "counter = 0\n",
    "\n",
    "# 记录训练开始时间\n",
    "start_time = time.time()\n",
    "\n",
    "# 训练历史记录\n",
    "train_losses = []\n",
    "train_accuracies = []\n",
    "val_losses = []\n",
    "val_accuracies = []\n",
    "\n",
    "# 训练模型\n",
    "num_epochs = 20\n",
    "validation_split = 0.2\n",
    "train_size = int(len(train_dataset) * (1 - validation_split))\n",
    "val_size = len(train_dataset) - train_size\n",
    "train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size])\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # 训练阶段\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "    train_correct = 0\n",
    "    train_total = 0\n",
    "    \n",
    "    for inputs, labels in train_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        train_loss += loss.item() * inputs.size(0)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        train_total += labels.size(0)\n",
    "        train_correct += (predicted == labels).sum().item()\n",
    "    \n",
    "    train_loss = train_loss / len(train_loader.dataset)\n",
    "    train_accuracy = train_correct / train_total\n",
    "    train_losses.append(train_loss)\n",
    "    train_accuracies.append(train_accuracy)\n",
    "    \n",
    "    # 验证阶段\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    val_correct = 0\n",
    "    val_total = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in val_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            val_loss += loss.item() * inputs.size(0)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            val_total += labels.size(0)\n",
    "            val_correct += (predicted == labels).sum().item()\n",
    "    \n",
    "    val_loss = val_loss / len(val_loader.dataset)\n",
    "    val_accuracy = val_correct / val_total\n",
    "    val_losses.append(val_loss)\n",
    "    val_accuracies.append(val_accuracy)\n",
    "    \n",
    "    print(f'Epoch {epoch+1}/{num_epochs}:')\n",
    "    print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_accuracy:.4f}')\n",
    "    print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_accuracy:.4f}')\n",
    "    \n",
    "    # 早停检查\n",
    "    if val_loss < best_val_loss:\n",
    "        best_val_loss = val_loss\n",
    "        counter = 0\n",
    "        # 保存最佳模型\n",
    "        torch.save(model.state_dict(), 'best_mnist_cnn_model.pth')\n",
    "    else:\n",
    "        counter += 1\n",
    "        if counter >= patience:\n",
    "            print(f'Early stopping at epoch {epoch+1}')\n",
    "            break\n",
    "\n",
    "# 计算训练时间\n",
    "training_time = time.time() - start_time\n",
    "print(f\"\\nCNN模型训练完成！耗时: {training_time:.2f} 秒\")\n",
    "\n",
    "# 加载最佳模型\n",
    "model.load_state_dict(torch.load('best_mnist_cnn_model.pth'))\n",
    "\n",
    "# 5. 评估模型\n",
    "print(\"\\n在测试集上评估模型性能...\")\n",
    "model.eval()\n",
    "test_loss = 0.0\n",
    "test_correct = 0\n",
    "test_total = 0\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in test_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        test_loss += loss.item() * inputs.size(0)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        test_total += labels.size(0)\n",
    "        test_correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        all_preds.extend(predicted.cpu().numpy())\n",
    "        all_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "test_loss = test_loss / len(test_loader.dataset)\n",
    "test_accuracy = test_correct / test_total\n",
    "\n",
    "print(f\"测试集准确率: {test_accuracy:.4f}\")\n",
    "print(f\"测试集损失: {test_loss:.4f}\")\n",
    "\n",
    "# 6. 可视化混淆矩阵\n",
    "conf_mat = confusion_matrix(all_labels, all_preds)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=range(10), yticklabels=range(10))\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 7. 显示分类报告\n",
    "print(\"\\n分类报告:\")\n",
    "print(classification_report(all_labels, all_preds))\n",
    "\n",
    "# 8. 可视化训练历史\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 绘制准确率曲线\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_accuracies, label='Train Accuracy')\n",
    "plt.plot(val_accuracies, label='Validation Accuracy')\n",
    "plt.title('Model Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "# 绘制损失曲线\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_losses, label='Train Loss')\n",
    "plt.plot(val_losses, label='Validation Loss')\n",
    "plt.title('Model Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cuda\n",
      "正在加载数据...\n",
      "训练集大小: (60000, 785)\n",
      "测试集大小: (10000, 785)\n",
      "\n",
      "数据预处理...\n",
      "处理后训练数据形状: (60000, 1, 28, 28)\n",
      "处理后测试数据形状: (10000, 1, 28, 28)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CNN模型结构:\n",
      "CNN(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (fc1): Linear(in_features=1152, out_features=128, bias=True)\n",
      "  (dropout): Dropout(p=0.5, inplace=False)\n",
      "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
      ")\n",
      "\n",
      "开始训练CNN模型...\n",
      "Epoch 1/20:\n",
      "Train Loss: 0.3069, Train Acc: 0.9012\n",
      "Val Loss: 0.0814, Val Acc: 0.9740\n",
      "Epoch 2/20:\n",
      "Train Loss: 0.0734, Train Acc: 0.9775\n",
      "Val Loss: 0.0561, Val Acc: 0.9822\n",
      "Epoch 3/20:\n",
      "Train Loss: 0.0533, Train Acc: 0.9832\n",
      "Val Loss: 0.0422, Val Acc: 0.9869\n",
      "Epoch 4/20:\n",
      "Train Loss: 0.0369, Train Acc: 0.9886\n",
      "Val Loss: 0.0391, Val Acc: 0.9888\n",
      "Epoch 5/20:\n",
      "Train Loss: 0.0309, Train Acc: 0.9904\n",
      "Val Loss: 0.0351, Val Acc: 0.9902\n",
      "Epoch 6/20:\n",
      "Train Loss: 0.0260, Train Acc: 0.9914\n",
      "Val Loss: 0.0355, Val Acc: 0.9902\n",
      "Epoch 7/20:\n",
      "Train Loss: 0.0210, Train Acc: 0.9938\n",
      "Val Loss: 0.0322, Val Acc: 0.9908\n",
      "Epoch 8/20:\n",
      "Train Loss: 0.0173, Train Acc: 0.9945\n",
      "Val Loss: 0.0325, Val Acc: 0.9906\n",
      "Epoch 9/20:\n",
      "Train Loss: 0.0160, Train Acc: 0.9949\n",
      "Val Loss: 0.0400, Val Acc: 0.9892\n",
      "Epoch 10/20:\n",
      "Train Loss: 0.0147, Train Acc: 0.9952\n",
      "Val Loss: 0.0332, Val Acc: 0.9912\n",
      "Epoch 11/20:\n",
      "Train Loss: 0.0120, Train Acc: 0.9960\n",
      "Val Loss: 0.0360, Val Acc: 0.9899\n",
      "Epoch 12/20:\n",
      "Train Loss: 0.0098, Train Acc: 0.9967\n",
      "Val Loss: 0.0393, Val Acc: 0.9907\n",
      "Early stopping at epoch 12\n",
      "\n",
      "CNN模型训练完成！耗时: 34.06 秒\n",
      "\n",
      "在测试集上评估模型性能...\n",
      "测试集准确率: 0.9935\n",
      "测试集损失: 0.0202\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00       980\n",
      "           1       0.99      1.00      1.00      1135\n",
      "           2       0.99      1.00      1.00      1032\n",
      "           3       0.99      1.00      1.00      1010\n",
      "           4       0.99      0.99      0.99       982\n",
      "           5       1.00      0.99      0.99       892\n",
      "           6       1.00      0.99      0.99       958\n",
      "           7       0.99      0.99      0.99      1028\n",
      "           8       1.00      0.99      1.00       974\n",
      "           9       0.99      0.99      0.99      1009\n",
      "\n",
      "    accuracy                           0.99     10000\n",
      "   macro avg       0.99      0.99      0.99     10000\n",
      "weighted avg       0.99      0.99      0.99     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# LSTM",
   "id": "4e198859bb389c9b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T05:34:50.478946Z",
     "start_time": "2025-06-12T05:34:03.128229Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "# 导入PyTorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torchvision import transforms\n",
    "\n",
    "# 设置随机种子以便结果可复现\n",
    "np.random.seed(42)\n",
    "torch.manual_seed(42)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(42)\n",
    "\n",
    "# 设置文件路径\n",
    "base_path = r\"C://Users/30979//Desktop//mnist_dataset\"\n",
    "train_path = os.path.join(base_path, \"mnist_train.csv\")\n",
    "test_path = os.path.join(base_path, \"mnist_test.csv\")\n",
    "\n",
    "# 检查是否有可用的GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"使用设备: {device}\")\n",
    "\n",
    "# 1. 加载数据\n",
    "print(\"正在加载数据...\")\n",
    "train_data = pd.read_csv(train_path)\n",
    "test_data = pd.read_csv(test_path)\n",
    "\n",
    "print(f\"训练集大小: {train_data.shape}\")\n",
    "print(f\"测试集大小: {test_data.shape}\")\n",
    "\n",
    "# 2. 数据预处理\n",
    "print(\"\\n数据预处理...\")\n",
    "# 分离特征和标签\n",
    "X_train = train_data.drop(columns=['label']).values.astype('float32')\n",
    "y_train = train_data['label'].values.astype('int64')\n",
    "X_test = test_data.drop(columns=['label']).values.astype('float32')\n",
    "y_test = test_data['label'].values.astype('int64')\n",
    "\n",
    "# 归一化像素值到0-1范围\n",
    "X_train = X_train / 255.0\n",
    "X_test = X_test / 255.0\n",
    "\n",
    "# 重塑数据为RNN所需的格式：[样本数, 时间步长, 特征]\n",
    "# 将每张图像视为28个时间步长，每步有28个特征\n",
    "X_train = X_train.reshape(-1, 28, 28)\n",
    "X_test = X_test.reshape(-1, 28, 28)\n",
    "\n",
    "print(f\"处理后训练数据形状: {X_train.shape}\")\n",
    "print(f\"处理后测试数据形状: {X_test.shape}\")\n",
    "\n",
    "# 可视化一些样本\n",
    "plt.figure(figsize=(10, 5))\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    plt.imshow(X_train[i], cmap='gray')\n",
    "    plt.title(f\"Label: {y_train[i]}\")\n",
    "    plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 3. 定义不同类型的RNN模型\n",
    "\n",
    "# 3.1 基本LSTM模型\n",
    "\n",
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_size=28, hidden_size=128, num_classes=10, dropout=0.2):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        \n",
    "        # 定义两个独立的LSTM层（非堆叠）\n",
    "        self.lstm1 = nn.LSTM(input_size, hidden_size, batch_first=True, dropout=dropout)\n",
    "        self.lstm2 = nn.LSTM(hidden_size, hidden_size//2, batch_first=True, dropout=dropout)\n",
    "        self.fc1 = nn.Linear(hidden_size//2, 32)\n",
    "        self.fc2 = nn.Linear(32, num_classes)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # x shape: (batch, seq_len, input_size)\n",
    "        batch_size = x.size(0)\n",
    "        \n",
    "        # 为第一个LSTM层初始化隐藏状态（单层）\n",
    "        h0_1 = torch.zeros(1, batch_size, self.hidden_size).to(x.device)\n",
    "        c0_1 = torch.zeros(1, batch_size, self.hidden_size).to(x.device)\n",
    "        \n",
    "        # 第一个LSTM层\n",
    "        out, _ = self.lstm1(x, (h0_1, c0_1))\n",
    "        out = self.dropout(out)\n",
    "        \n",
    "        # 为第二个LSTM层初始化隐藏状态（单层）\n",
    "        hidden_size_2 = self.hidden_size // 2\n",
    "        h0_2 = torch.zeros(1, batch_size, hidden_size_2).to(x.device)\n",
    "        c0_2 = torch.zeros(1, batch_size, hidden_size_2).to(x.device)\n",
    "        \n",
    "        # 第二个LSTM层\n",
    "        out, _ = self.lstm2(out, (h0_2, c0_2))\n",
    "        out = self.dropout(out)\n",
    "        \n",
    "        # 取最后一个时间步的输出\n",
    "        out = out[:, -1, :]\n",
    "        \n",
    "        # 全连接层\n",
    "        out = self.relu(self.fc1(out))\n",
    "        out = self.dropout(out)\n",
    "        out = self.fc2(out)\n",
    "        return out\n",
    "\n",
    "# 3.2 双向LSTM模型\n",
    "class BiLSTMModel(nn.Module):\n",
    "    def __init__(self, input_size=28, hidden_size=128, num_layers=2, num_classes=10, dropout=0.2):\n",
    "        super(BiLSTMModel, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        \n",
    "        self.lstm1 = nn.LSTM(input_size, hidden_size, batch_first=True, bidirectional=True, dropout=dropout)\n",
    "        self.lstm2 = nn.LSTM(hidden_size*2, hidden_size//2, batch_first=True, bidirectional=True, dropout=dropout)\n",
    "        self.fc1 = nn.Linear((hidden_size//2)*2, 32)\n",
    "        self.fc2 = nn.Linear(32, num_classes)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # x shape: (batch, seq_len, input_size)\n",
    "        h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(x.device)\n",
    "        c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(x.device)\n",
    "        \n",
    "        # 双向LSTM层1\n",
    "        out, _ = self.lstm1(x, (h0, c0))\n",
    "        out = self.dropout(out)\n",
    "        \n",
    "        # 双向LSTM层2\n",
    "        out, _ = self.lstm2(out)\n",
    "        out = self.dropout(out)\n",
    "        \n",
    "        # 取最后一个时间步的输出\n",
    "        out = out[:, -1, :]\n",
    "        \n",
    "        # 全连接层\n",
    "        out = self.relu(self.fc1(out))\n",
    "        out = self.dropout(out)\n",
    "        out = self.fc2(out)\n",
    "        return out\n",
    "\n",
    "# 3.3 GRU模型\n",
    "class GRUModel(nn.Module):\n",
    "    def __init__(self, input_size=28, hidden_size=128, num_layers=2, num_classes=10, dropout=0.2):\n",
    "        super(GRUModel, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        \n",
    "        self.gru1 = nn.GRU(input_size, hidden_size, batch_first=True, dropout=dropout)\n",
    "        self.gru2 = nn.GRU(hidden_size, hidden_size//2, batch_first=True, dropout=dropout)\n",
    "        self.fc1 = nn.Linear(hidden_size//2, 32)\n",
    "        self.fc2 = nn.Linear(32, num_classes)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # x shape: (batch, seq_len, input_size)\n",
    "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)\n",
    "        \n",
    "        # GRU层1\n",
    "        out, _ = self.gru1(x, h0)\n",
    "        out = self.dropout(out)\n",
    "        \n",
    "        # GRU层2\n",
    "        out, _ = self.gru2(out)\n",
    "        out = self.dropout(out)\n",
    "        \n",
    "        # 取最后一个时间步的输出\n",
    "        out = out[:, -1, :]\n",
    "        \n",
    "        # 全连接层\n",
    "        out = self.relu(self.fc1(out))\n",
    "        out = self.dropout(out)\n",
    "        out = self.fc2(out)\n",
    "        return out\n",
    "\n",
    "# 选择要使用的模型\n",
    "# 可以替换为 BiLSTMModel() 或 GRUModel()\n",
    "model = LSTMModel()\n",
    "model_name = \"LSTM\"  # 相应地更改此名称\n",
    "\n",
    "# 将模型移至GPU（如果可用）\n",
    "model = model.to(device)\n",
    "\n",
    "# 打印模型结构\n",
    "print(f\"\\n{model_name}模型结构:\")\n",
    "print(model)\n",
    "\n",
    "# 4. 训练模型\n",
    "print(f\"\\n开始训练{model_name}模型...\")\n",
    "\n",
    "# 创建数据集和数据加载器\n",
    "X_train_tensor = torch.tensor(X_train)\n",
    "y_train_tensor = torch.tensor(y_train)\n",
    "X_test_tensor = torch.tensor(X_test)\n",
    "y_test_tensor = torch.tensor(y_test)\n",
    "\n",
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "test_dataset = TensorDataset(X_test_tensor, y_test_tensor)\n",
    "\n",
    "batch_size = 128\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters())\n",
    "\n",
    "# 设置早停参数\n",
    "patience = 5\n",
    "best_val_loss = float('inf')\n",
    "counter = 0\n",
    "\n",
    "# 记录训练开始时间\n",
    "start_time = time.time()\n",
    "\n",
    "# 训练历史记录\n",
    "train_losses = []\n",
    "train_accuracies = []\n",
    "val_losses = []\n",
    "val_accuracies = []\n",
    "\n",
    "# 训练模型\n",
    "num_epochs = 15\n",
    "validation_split = 0.2\n",
    "train_size = int(len(train_dataset) * (1 - validation_split))\n",
    "val_size = len(train_dataset) - train_size\n",
    "train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size])\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # 训练阶段\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "    train_correct = 0\n",
    "    train_total = 0\n",
    "    \n",
    "    for inputs, labels in train_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        train_loss += loss.item() * inputs.size(0)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        train_total += labels.size(0)\n",
    "        train_correct += (predicted == labels).sum().item()\n",
    "    \n",
    "    train_loss = train_loss / len(train_loader.dataset)\n",
    "    train_accuracy = train_correct / train_total\n",
    "    train_losses.append(train_loss)\n",
    "    train_accuracies.append(train_accuracy)\n",
    "    \n",
    "    # 验证阶段\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    val_correct = 0\n",
    "    val_total = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in val_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            val_loss += loss.item() * inputs.size(0)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            val_total += labels.size(0)\n",
    "            val_correct += (predicted == labels).sum().item()\n",
    "    \n",
    "    val_loss = val_loss / len(val_loader.dataset)\n",
    "    val_accuracy = val_correct / val_total\n",
    "    val_losses.append(val_loss)\n",
    "    val_accuracies.append(val_accuracy)\n",
    "    \n",
    "    print(f'Epoch {epoch+1}/{num_epochs}:')\n",
    "    print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_accuracy:.4f}')\n",
    "    print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_accuracy:.4f}')\n",
    "    \n",
    "    # 早停检查\n",
    "    if val_loss < best_val_loss:\n",
    "        best_val_loss = val_loss\n",
    "        counter = 0\n",
    "        # 保存最佳模型\n",
    "        torch.save(model.state_dict(), f'best_mnist_{model_name.lower()}_model.pth')\n",
    "    else:\n",
    "        counter += 1\n",
    "        if counter >= patience:\n",
    "            print(f'Early stopping at epoch {epoch+1}')\n",
    "            break\n",
    "\n",
    "# 计算训练时间\n",
    "training_time = time.time() - start_time\n",
    "print(f\"\\n{model_name}模型训练完成！耗时: {training_time:.2f} 秒\")\n",
    "\n",
    "# 加载最佳模型\n",
    "model.load_state_dict(torch.load(f'best_mnist_{model_name.lower()}_model.pth'))\n",
    "\n",
    "# 5. 评估模型\n",
    "print(\"\\n在测试集上评估模型性能...\")\n",
    "model.eval()\n",
    "test_loss = 0.0\n",
    "test_correct = 0\n",
    "test_total = 0\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in test_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        test_loss += loss.item() * inputs.size(0)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        test_total += labels.size(0)\n",
    "        test_correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        all_preds.extend(predicted.cpu().numpy())\n",
    "        all_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "test_loss = test_loss / len(test_loader.dataset)\n",
    "test_accuracy = test_correct / test_total\n",
    "\n",
    "print(f\"测试集准确率: {test_accuracy:.4f}\")\n",
    "print(f\"测试集损失: {test_loss:.4f}\")\n",
    "\n",
    "# 6. 可视化混淆矩阵\n",
    "conf_mat = confusion_matrix(all_labels, all_preds)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=range(10), yticklabels=range(10))\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title(f'{model_name} Confusion Matrix')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 7. 显示分类报告\n",
    "print(\"\\n分类报告:\")\n",
    "print(classification_report(all_labels, all_preds))\n",
    "\n",
    "# 8. 可视化训练历史\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 绘制准确率曲线\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_accuracies, label='Train Accuracy')\n",
    "plt.plot(val_accuracies, label='Validation Accuracy')\n",
    "plt.title(f'{model_name} Model Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "# 绘制损失曲线\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_losses, label='Train Loss')\n",
    "plt.plot(val_losses, label='Validation Loss')\n",
    "plt.title(f'{model_name} Model Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "6955385d8a85a033",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cuda\n",
      "正在加载数据...\n",
      "训练集大小: (60000, 785)\n",
      "测试集大小: (10000, 785)\n",
      "\n",
      "数据预处理...\n",
      "处理后训练数据形状: (60000, 28, 28)\n",
      "处理后测试数据形状: (10000, 28, 28)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\an\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:123: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LSTM模型结构:\n",
      "LSTMModel(\n",
      "  (lstm1): LSTM(28, 128, batch_first=True, dropout=0.2)\n",
      "  (lstm2): LSTM(128, 64, batch_first=True, dropout=0.2)\n",
      "  (fc1): Linear(in_features=64, out_features=32, bias=True)\n",
      "  (fc2): Linear(in_features=32, out_features=10, bias=True)\n",
      "  (relu): ReLU()\n",
      "  (dropout): Dropout(p=0.2, inplace=False)\n",
      ")\n",
      "\n",
      "开始训练LSTM模型...\n",
      "Epoch 1/15:\n",
      "Train Loss: 0.9018, Train Acc: 0.7107\n",
      "Val Loss: 0.2896, Val Acc: 0.9132\n",
      "Epoch 2/15:\n",
      "Train Loss: 0.2823, Train Acc: 0.9254\n",
      "Val Loss: 0.1716, Val Acc: 0.9523\n",
      "Epoch 3/15:\n",
      "Train Loss: 0.1829, Train Acc: 0.9528\n",
      "Val Loss: 0.1359, Val Acc: 0.9610\n",
      "Epoch 4/15:\n",
      "Train Loss: 0.1331, Train Acc: 0.9652\n",
      "Val Loss: 0.0999, Val Acc: 0.9722\n",
      "Epoch 5/15:\n",
      "Train Loss: 0.1107, Train Acc: 0.9719\n",
      "Val Loss: 0.0997, Val Acc: 0.9709\n",
      "Epoch 6/15:\n",
      "Train Loss: 0.0947, Train Acc: 0.9752\n",
      "Val Loss: 0.0863, Val Acc: 0.9742\n",
      "Epoch 7/15:\n",
      "Train Loss: 0.0803, Train Acc: 0.9788\n",
      "Val Loss: 0.0687, Val Acc: 0.9809\n",
      "Epoch 8/15:\n",
      "Train Loss: 0.0689, Train Acc: 0.9818\n",
      "Val Loss: 0.0631, Val Acc: 0.9825\n",
      "Epoch 9/15:\n",
      "Train Loss: 0.0638, Train Acc: 0.9835\n",
      "Val Loss: 0.0557, Val Acc: 0.9846\n",
      "Epoch 10/15:\n",
      "Train Loss: 0.0592, Train Acc: 0.9842\n",
      "Val Loss: 0.0725, Val Acc: 0.9791\n",
      "Epoch 11/15:\n",
      "Train Loss: 0.0553, Train Acc: 0.9855\n",
      "Val Loss: 0.0763, Val Acc: 0.9799\n",
      "Epoch 12/15:\n",
      "Train Loss: 0.0503, Train Acc: 0.9865\n",
      "Val Loss: 0.0557, Val Acc: 0.9839\n",
      "Epoch 13/15:\n",
      "Train Loss: 0.0437, Train Acc: 0.9886\n",
      "Val Loss: 0.0607, Val Acc: 0.9844\n",
      "Epoch 14/15:\n",
      "Train Loss: 0.0422, Train Acc: 0.9884\n",
      "Val Loss: 0.0522, Val Acc: 0.9861\n",
      "Epoch 15/15:\n",
      "Train Loss: 0.0392, Train Acc: 0.9894\n",
      "Val Loss: 0.0548, Val Acc: 0.9857\n",
      "\n",
      "LSTM模型训练完成！耗时: 44.11 秒\n",
      "\n",
      "在测试集上评估模型性能...\n",
      "测试集准确率: 0.9849\n",
      "测试集损失: 0.0576\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       980\n",
      "           1       0.99      1.00      0.99      1135\n",
      "           2       0.98      0.99      0.98      1032\n",
      "           3       0.99      0.98      0.98      1010\n",
      "           4       0.98      0.99      0.99       982\n",
      "           5       0.98      0.98      0.98       892\n",
      "           6       0.99      0.98      0.98       958\n",
      "           7       0.99      0.98      0.98      1028\n",
      "           8       0.99      0.97      0.98       974\n",
      "           9       0.99      0.98      0.98      1009\n",
      "\n",
      "    accuracy                           0.98     10000\n",
      "   macro avg       0.98      0.98      0.98     10000\n",
      "weighted avg       0.98      0.98      0.98     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# MLP",
   "id": "9154c76018a5d0f0"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T05:38:44.526727Z",
     "start_time": "2025-06-12T05:38:10.903662Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "# 导入PyTorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "# 设置随机种子以便结果可复现\n",
    "np.random.seed(42)\n",
    "torch.manual_seed(42)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(42)\n",
    "\n",
    "# 设置文件路径\n",
    "base_path = r\"C://Users/30979//Desktop//mnist_dataset\"\n",
    "train_path = os.path.join(base_path, \"mnist_train.csv\")\n",
    "test_path = os.path.join(base_path, \"mnist_test.csv\")\n",
    "\n",
    "# 检查是否有可用的GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"使用设备: {device}\")\n",
    "\n",
    "# 1. 加载数据\n",
    "print(\"正在加载数据...\")\n",
    "train_data = pd.read_csv(train_path)\n",
    "test_data = pd.read_csv(test_path)\n",
    "\n",
    "print(f\"训练集大小: {train_data.shape}\")\n",
    "print(f\"测试集大小: {test_data.shape}\")\n",
    "\n",
    "# 2. 数据预处理\n",
    "print(\"\\n数据预处理...\")\n",
    "# 分离特征和标签\n",
    "X_train = train_data.drop(columns=['label']).values.astype('float32')\n",
    "y_train = train_data['label'].values.astype('int64')\n",
    "X_test = test_data.drop(columns=['label']).values.astype('float32')\n",
    "y_test = test_data['label'].values.astype('int64')\n",
    "\n",
    "# 归一化像素值到0-1范围\n",
    "X_train = X_train / 255.0\n",
    "X_test = X_test / 255.0\n",
    "\n",
    "print(f\"处理后训练数据形状: {X_train.shape}\")\n",
    "print(f\"处理后测试数据形状: {X_test.shape}\")\n",
    "\n",
    "# 可视化一些样本\n",
    "plt.figure(figsize=(10, 5))\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    plt.imshow(X_train[i].reshape(28, 28), cmap='gray')\n",
    "    plt.title(f\"Label: {y_train[i]}\")\n",
    "    plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 3. 定义MLP模型\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_size=784, hidden_size1=512, hidden_size2=256, hidden_size3=128, num_classes=10):\n",
    "        super(MLP, self).__init__()\n",
    "        # 定义网络层\n",
    "        self.fc1 = nn.Linear(input_size, hidden_size1)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.dropout1 = nn.Dropout(0.2)\n",
    "        \n",
    "        self.fc2 = nn.Linear(hidden_size1, hidden_size2)\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.dropout2 = nn.Dropout(0.2)\n",
    "        \n",
    "        self.fc3 = nn.Linear(hidden_size2, hidden_size3)\n",
    "        self.relu3 = nn.ReLU()\n",
    "        self.dropout3 = nn.Dropout(0.2)\n",
    "        \n",
    "        self.fc4 = nn.Linear(hidden_size3, num_classes)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # 前向传播\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu1(x)\n",
    "        x = self.dropout1(x)\n",
    "        \n",
    "        x = self.fc2(x)\n",
    "        x = self.relu2(x)\n",
    "        x = self.dropout2(x)\n",
    "        \n",
    "        x = self.fc3(x)\n",
    "        x = self.relu3(x)\n",
    "        x = self.dropout3(x)\n",
    "        \n",
    "        x = self.fc4(x)\n",
    "        return x\n",
    "\n",
    "# 4. 初始化模型并移至GPU（如果可用）\n",
    "model = MLP().to(device)\n",
    "\n",
    "# 打印模型结构\n",
    "print(\"\\nMLP模型结构:\")\n",
    "print(model)\n",
    "\n",
    "# 5. 训练模型\n",
    "print(\"\\n开始训练MLP模型...\")\n",
    "# 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters())\n",
    "\n",
    "# 设置早停参数\n",
    "patience = 5\n",
    "best_val_loss = float('inf')\n",
    "counter = 0\n",
    "\n",
    "# 记录训练开始时间\n",
    "start_time = time.time()\n",
    "\n",
    "# 训练历史记录\n",
    "train_losses = []\n",
    "train_accuracies = []\n",
    "val_losses = []\n",
    "val_accuracies = []\n",
    "\n",
    "# 创建数据集和数据加载器\n",
    "X_train_tensor = torch.tensor(X_train)\n",
    "y_train_tensor = torch.tensor(y_train)\n",
    "X_test_tensor = torch.tensor(X_test)\n",
    "y_test_tensor = torch.tensor(y_test)\n",
    "\n",
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "test_dataset = TensorDataset(X_test_tensor, y_test_tensor)\n",
    "\n",
    "batch_size = 128\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "# 训练模型\n",
    "num_epochs = 20\n",
    "validation_split = 0.2\n",
    "train_size = int(len(train_dataset) * (1 - validation_split))\n",
    "val_size = len(train_dataset) - train_size\n",
    "train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size])\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # 训练阶段\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "    train_correct = 0\n",
    "    train_total = 0\n",
    "    \n",
    "    for inputs, labels in train_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        train_loss += loss.item() * inputs.size(0)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        train_total += labels.size(0)\n",
    "        train_correct += (predicted == labels).sum().item()\n",
    "    \n",
    "    train_loss = train_loss / len(train_loader.dataset)\n",
    "    train_accuracy = train_correct / train_total\n",
    "    train_losses.append(train_loss)\n",
    "    train_accuracies.append(train_accuracy)\n",
    "    \n",
    "    # 验证阶段\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    val_correct = 0\n",
    "    val_total = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in val_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            val_loss += loss.item() * inputs.size(0)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            val_total += labels.size(0)\n",
    "            val_correct += (predicted == labels).sum().item()\n",
    "    \n",
    "    val_loss = val_loss / len(val_loader.dataset)\n",
    "    val_accuracy = val_correct / val_total\n",
    "    val_losses.append(val_loss)\n",
    "    val_accuracies.append(val_accuracy)\n",
    "    \n",
    "    print(f'Epoch {epoch+1}/{num_epochs}:')\n",
    "    print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_accuracy:.4f}')\n",
    "    print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_accuracy:.4f}')\n",
    "    \n",
    "    # 早停检查\n",
    "    if val_loss < best_val_loss:\n",
    "        best_val_loss = val_loss\n",
    "        counter = 0\n",
    "        # 保存最佳模型\n",
    "        torch.save(model.state_dict(), 'best_mnist_mlp_model.pth')\n",
    "    else:\n",
    "        counter += 1\n",
    "        if counter >= patience:\n",
    "            print(f'Early stopping at epoch {epoch+1}')\n",
    "            break\n",
    "\n",
    "# 计算训练时间\n",
    "training_time = time.time() - start_time\n",
    "print(f\"\\nMLP模型训练完成！耗时: {training_time:.2f} 秒\")\n",
    "\n",
    "# 加载最佳模型\n",
    "model.load_state_dict(torch.load('best_mnist_mlp_model.pth'))\n",
    "\n",
    "# 6. 评估模型\n",
    "print(\"\\n在测试集上评估模型性能...\")\n",
    "model.eval()\n",
    "test_loss = 0.0\n",
    "test_correct = 0\n",
    "test_total = 0\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in test_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        test_loss += loss.item() * inputs.size(0)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        test_total += labels.size(0)\n",
    "        test_correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        all_preds.extend(predicted.cpu().numpy())\n",
    "        all_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "test_loss = test_loss / len(test_loader.dataset)\n",
    "test_accuracy = test_correct / test_total\n",
    "\n",
    "print(f\"测试集准确率: {test_accuracy:.4f}\")\n",
    "print(f\"测试集损失: {test_loss:.4f}\")\n",
    "\n",
    "# 7. 可视化混淆矩阵\n",
    "conf_mat = confusion_matrix(all_labels, all_preds)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=range(10), yticklabels=range(10))\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('MLP Confusion Matrix')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 8. 显示分类报告\n",
    "print(\"\\n分类报告:\")\n",
    "print(classification_report(all_labels, all_preds))\n",
    "\n",
    "# 9. 可视化训练历史\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 绘制准确率曲线\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_accuracies, label='Train Accuracy')\n",
    "plt.plot(val_accuracies, label='Validation Accuracy')\n",
    "plt.title('MLP Model Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "# 绘制损失曲线\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_losses, label='Train Loss')\n",
    "plt.plot(val_losses, label='Validation Loss')\n",
    "plt.title('MLP Model Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "1eb5faa6935f26b7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cuda\n",
      "正在加载数据...\n",
      "训练集大小: (60000, 785)\n",
      "测试集大小: (10000, 785)\n",
      "\n",
      "数据预处理...\n",
      "处理后训练数据形状: (60000, 784)\n",
      "处理后测试数据形状: (10000, 784)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MLP模型结构:\n",
      "MLP(\n",
      "  (fc1): Linear(in_features=784, out_features=512, bias=True)\n",
      "  (relu1): ReLU()\n",
      "  (dropout1): Dropout(p=0.2, inplace=False)\n",
      "  (fc2): Linear(in_features=512, out_features=256, bias=True)\n",
      "  (relu2): ReLU()\n",
      "  (dropout2): Dropout(p=0.2, inplace=False)\n",
      "  (fc3): Linear(in_features=256, out_features=128, bias=True)\n",
      "  (relu3): ReLU()\n",
      "  (dropout3): Dropout(p=0.2, inplace=False)\n",
      "  (fc4): Linear(in_features=128, out_features=10, bias=True)\n",
      ")\n",
      "\n",
      "开始训练MLP模型...\n",
      "Epoch 1/20:\n",
      "Train Loss: 0.3953, Train Acc: 0.8813\n",
      "Val Loss: 0.1807, Val Acc: 0.9475\n",
      "Epoch 2/20:\n",
      "Train Loss: 0.1442, Train Acc: 0.9565\n",
      "Val Loss: 0.1137, Val Acc: 0.9647\n",
      "Epoch 3/20:\n",
      "Train Loss: 0.0999, Train Acc: 0.9696\n",
      "Val Loss: 0.0931, Val Acc: 0.9732\n",
      "Epoch 4/20:\n",
      "Train Loss: 0.0782, Train Acc: 0.9764\n",
      "Val Loss: 0.0899, Val Acc: 0.9732\n",
      "Epoch 5/20:\n",
      "Train Loss: 0.0646, Train Acc: 0.9806\n",
      "Val Loss: 0.0857, Val Acc: 0.9757\n",
      "Epoch 6/20:\n",
      "Train Loss: 0.0562, Train Acc: 0.9825\n",
      "Val Loss: 0.0930, Val Acc: 0.9733\n",
      "Epoch 7/20:\n",
      "Train Loss: 0.0453, Train Acc: 0.9856\n",
      "Val Loss: 0.0763, Val Acc: 0.9791\n",
      "Epoch 8/20:\n",
      "Train Loss: 0.0424, Train Acc: 0.9870\n",
      "Val Loss: 0.0951, Val Acc: 0.9743\n",
      "Epoch 9/20:\n",
      "Train Loss: 0.0382, Train Acc: 0.9877\n",
      "Val Loss: 0.0811, Val Acc: 0.9785\n",
      "Epoch 10/20:\n",
      "Train Loss: 0.0342, Train Acc: 0.9891\n",
      "Val Loss: 0.0760, Val Acc: 0.9784\n",
      "Epoch 11/20:\n",
      "Train Loss: 0.0269, Train Acc: 0.9916\n",
      "Val Loss: 0.0820, Val Acc: 0.9793\n",
      "Epoch 12/20:\n",
      "Train Loss: 0.0288, Train Acc: 0.9910\n",
      "Val Loss: 0.0836, Val Acc: 0.9798\n",
      "Epoch 13/20:\n",
      "Train Loss: 0.0280, Train Acc: 0.9912\n",
      "Val Loss: 0.0855, Val Acc: 0.9799\n",
      "Epoch 14/20:\n",
      "Train Loss: 0.0239, Train Acc: 0.9925\n",
      "Val Loss: 0.0812, Val Acc: 0.9802\n",
      "Epoch 15/20:\n",
      "Train Loss: 0.0218, Train Acc: 0.9930\n",
      "Val Loss: 0.0948, Val Acc: 0.9791\n",
      "Early stopping at epoch 15\n",
      "\n",
      "MLP模型训练完成！耗时: 30.14 秒\n",
      "\n",
      "在测试集上评估模型性能...\n",
      "测试集准确率: 0.9807\n",
      "测试集损失: 0.0696\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99       980\n",
      "           1       0.99      0.99      0.99      1135\n",
      "           2       0.97      0.98      0.98      1032\n",
      "           3       0.98      0.99      0.98      1010\n",
      "           4       0.98      0.98      0.98       982\n",
      "           5       0.97      0.98      0.98       892\n",
      "           6       0.99      0.98      0.99       958\n",
      "           7       0.98      0.97      0.98      1028\n",
      "           8       0.98      0.97      0.97       974\n",
      "           9       0.98      0.97      0.97      1009\n",
      "\n",
      "    accuracy                           0.98     10000\n",
      "   macro avg       0.98      0.98      0.98     10000\n",
      "weighted avg       0.98      0.98      0.98     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": 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PP/Dnn39y0003YbFYPDMj+/r60rx5cw4cOMCHH37IZZddVsxoi8iplJQSqYEuueQSQkJCqFevHk2aNDnrtr///ju///57oeWTJ08u9QsicF+srV279rQzvuRr2rQp//3vf3n66aeZNm0aBoOBtm3b8tZbb9G5c2fPdgsXLmTBggU8//zz2Gw2hgwZwnXXXcdXX33l2ebaa68lMDCQ1157jffee4+AgAA6duzIggULznhBlu/YsWN8//33jBo16oxJoWHDhvHCCy+wfPlyunbtyvz581m4cCFvvvkmycnJNGnShOeff54BAwYAcMMNNxAQEMCSJUt47733qFu3Lrfeeiu33nor4J4tZurUqbz99tt8/vnnngukUaNGnTO2L730Eo899hgPPPAAfn5+XHjhhbz88ss8/vjj/PLLL9x4443Uq1eP//73v8yfP9+zXdeuXXnmmWe8htJHRUXRokULIiMjiYqKOuexRURERNdgpXUN9k+rV69m9erVhZZfdNFFREVFsXjxYp5//nneeOMNkpKSiI2N5Z577uHmm28GwGw2s3TpUp566ikee+wx0tLSaNSoEXPmzGHEiBEAzJkzh2effZalS5eSmJhIREQEI0eOZPLkycXqq4h4M7jyq9uKiIgU0bFjx+jbt69XQk1ERERERKQ4lJQSEZEi+/PPP/nqq6/4/PPPcTgcrF69GqNR5QlFRERERKT4dCchIiJFZrPZeP3113E4HDz99NNKSImIiIiISIlppJSIiIiIiIiIiJQ7fcUtIiIiIiIiIiLlTkkpEREREREREREpd0pKiYiIiIiIiIhIuVNSSkREREREREREyp2SUiIiIiIiIiIiUu5MFd2ByuTkyXTKYi5CgwEiIoLLrP2qRvEooFh4Uzy8KR4FFAtvioe3so5HfvtyZrqGKnuKhTfFw5viUUCx8KZ4eFM8ClSW6yclpU7hclGmv5hl3X5Vo3gUUCy8KR7eFI8CioU3xcOb4lFxdA1VfhQLb4qHN8WjgGLhTfHwpngUqOhY6PE9EREREREREREpd0pKiYiIiIiIiIhIuVNSSkREREREREREyp1qShWD0+nE4bAXez+DAbKzs8nNzdFzqygepyqtWPj4mDAalWMWEREREZGSK+k9b1Whe9EC5xuL0roHVVKqCFwuF2lpSVitGSVuIynJiNPpLMVeVW2KR4HSioW/fxAhIbUwGAyl0CsREREREakpSuOet6rQvWiB841FadyDKilVBPl/nEFB4fj5mUsUcB8fAw5HDU/FnkLxKHC+sXC5XOTk2MjISAYgNDSitLomIiIiIiI1QGnc81YVuhctUNJYlOY9qJJS5+B0Ojx/nEFBISVux2QyYrcrG5tP8ShQGrHw8zMDkJGRTHBwuB7lExGp5mw2G7Nnz+aLL77AYrEwduxYxo4de9Z9jhw5wtChQ1m0aBFdu3b1LH/jjTdYsmQJGRkZDB48mIceegh/f/+yPgUREakkSuuet6rQvWiB84lFad2D6s71HBwOB1AQcJHKKv93tDo/Ay4iIm7z5s1jx44dvPnmmzz88MMsXLiQtWvXnnWfWbNmkZWV5bXs888/Z+HChcyZM4c333yTbdu2MX/+/LLsuoiIVDK655WSKo17UCWliqg6D1+U6kG/oyIiNUNWVhYrVqxgxowZtG7dmoEDBzJu3DiWLVt2xn0++eQTMjMzCy1/6623GD16NH379qVt27bMnj2b999/H6vVWpanICIilZDuJ6S4SuN3RkkpERERkSpk165d2O12OnTo4FnWqVMntm3bdtpipcnJycyfP585c+Z4LXc4HPz+++907tzZs6x9+/bk5uaya9eusjsBERERkTyqKVVNPfbYLD77bPUZ1z///CI6dux8xvWnM3HibXTo0Ilbbrm9RH2yWq0MHTqQZs1a8MorS0vUhoiISE2XmJhIeHg4fn5+nmWRkZHYbDZSUlKoVauW1/ZPPvkkV199NU2bNvVanpaWhs1mo06dOp5lJpOJsLAwEhISitWnsvpyPb9dfXmvWPyT4uFN8SigWHgrSjyqYqwq2/3uyJFDGTv2NoYMGVrsfasDg6Hw71FRf6+UlKqmJk++jzvumAjAV199yfLl7/Dqq2961oeEhBa7zccfn4/J5FviPn333TdERETy++/biIs7QlRUdInbEhERqamsVqtXQgrwfM7JyfFa/sMPP7BlyxZWry584Z6dne2176lt/bOdc4mICC7W9sVV1u1XJYqFN8XDm+JRQLHwdrZ4ZGdnk5RkxMfHgMlUNR6muvfe+5k48T8ArFv3BcuWvc3rr7/tWR8SEnrWczndurlzn8LX17fEMTAaq078TnU+fXY6DRiNRsLDA7FYLCU7fomPLpVaUFAQQUFBnvdGo5GIiMjzarMkiaxTrVv3Ob16XcrPP2/i009Xc/PNt51XeyIiIjWR2WwulDTK/3zqBWF2djYzZ87k4YcfPu2Fotls9tr31LaKO/veyZPpuMpgdm2DwX0jVVbtVyWKhTfFw5viUUCx8FaUeOTm5uB0OnE4XFVmVjqLJRCLJRAAf/9AjEYjoaHeI4XPdC5nmnEuMDD4rPudi9NZdeKX73xnInQ4XDidTpKTM/H1zfVal/+7dy5VL40npSI+/ig9e3bmjTde4/LL+/L003NxuVy89dZSrr32Ki69tBvDhl3O0qWvePaZOPE2lixZDLiHS77wwtPMnDmN/v17MGLEFaxdu+aMx0tLS2Pz5p9o374Dl1zSk88+W4PrH/9X/PzzT/n3v6+hf/8e3HHHWP76q6CexfLl7zBy5FAGDuzFPfdM5OjRuEJ9OvW84uOPAtCzZ2dee20RV1zRn6lT7wZg1aqP+Pe/r+HSS7txxRX9eeqpuZ4ZJ850rO3bt9KnT1eSk5M92+3a9Sf9+/cgK6tw4VgREZGyEhUVRXJyMnZ7wUw3iYmJWCwWQkIKpvLevn07hw8f5j//+Q8dOnTw1KC69dZbmTlzJmFhYZjNZk6cOOHZx263k5KSQu3atYvVJ5er7F5l3X5VeikWiofioViUVTyqm7Pd71599ZWlfr97Ljt2bGf8+FsYMKAn1157FR99tNKzLiEhgbvvnsDAgb248sqBPPPMPM+/8Xv2/MUdd4ylf/8eDB8+mNdff7XEfShL5/N7paRUCblcLqy5jqK/coqx7Rle/0zilIbt27exZMnbXHvtv1i7dg3/+9+7TJ36IO+++wE33zyOpUtfYffu0xc7ff/9/9G8eQveeus9+vTpx/z5j5ORkXHabTduXI/RaKRz56706tWHo0fj2LbtN8/6TZt+5Ikn5nDddf/izTeX06JFS6ZMuZvc3Fw++uh9Xn/9VcaPn8TSpcsICAjkoYceKPI5fv/9Rl5+eQl33DGJ337bwrPPzuf22yfw7rsfcN9901iz5mO+++4bgDMeq02bdkRG1mbjxg2edtev/5Lu3XsSEBBY5L6IiMj5cblc5NidpGTlcjQ1m72JmeyITyM713HunauJli1bYjKZ2Lp1q2fZli1baNOmDUZjwaVd27Zt+eKLL/joo488L4BHH32UyZMnYzQaadOmDVu2bPHss3XrVkwmEy1atCiv0zmrIylWHM5qeLckIlLJFft+txRe5XW/O336Q6V+v3s2Bw8e4D//GU/79h1ZuvQdxo69jYULn+Wbb9z3ls8+Ow9//wBef/2/PPHEAr7++is++eRDAB599GGaNm3O22//jwceeIhly97kxx+/K3lAKiE9vlcCLpeLccu3sf1oWrket110CK+OaleqU3Ved92/iImJBSAx8TjTpz9M585dABg+fCSvv/4qBw7so3nzwhenF17YjBtuGA3AuHG3s2LFuxw4sI82bdoV2vbLL7/g4ou7YrFYaNmyNXXqRPHZZ6tp374jAB9//AEDB17O8OEjAZgw4S5MJl/S0lL55JMPuO66f9O//yAA7rlnCu+++w42W3aRznHYsBE0aNAIcI9ueuCBh+jTpx8A9epFs3z5Mg4c2E+fPv3OeKycHBv9+w9iw4Z1DBs2AoANG75iwoT/FKkPIiI1lTPvojYrJ+91yntrroPMU3/+Y73nfa739qdLUvRtXpv5Q1tWwBmWP39/f4YPH86sWbN4/PHHOX78OEuXLuWJJ54A3KOmgoODsVgsNGzYsND+UVFRREREAPDvf/+bmTNn0qxZM+rUqcOsWbO47rrriv34Xln4Zu9J7vt4J5P7N+XGDvUqujsiIjVGdb/fvfjirtjtzlK93z2bVas+pFmz5tx++wQAGjRoxMGDB/jvf9+iT5++xMfH07x5C+rWrUdsbH3mz3+O4GD3yOeEhKP06tWHunXrER0dw7PPvkS9etWrNrOSUiVUBScoOK1Tf6E7duzMzp07WLRoIX//fYC//trNyZMnTzu9NEBsbH3P+8BAd/2qUx8lyHfy5Am2bt3ClCkzADAYDPTp05c1a1Zx991TsFgsHDr0N8OHj/Ds4+vry8SJdwFw6NDfjB1bcKNRq1YEEyZMLvI51q1bcI4tWrTEbDazZMliDhzYx759ezly5DBdunQ757EGDryM995bRmpqCkePxpGamkL37j2L3A8RkarO7nCSkG4jLjXb/UrJJtWaS2aOg6xcO9acfySZch1Yc8uutoLZZCTA14cAPx8ubV7n3DtUI9OmTWPWrFmMHj2aoKAgJk2axKBB7i9UevbsyRNPPMGIESPO0QpcccUVxMXFMXPmTHJychg0aBD3339/WXe/SNJt7toUv/ydpKSUiEg5q873uy+99AIHDuwvtfvdczl48CCtWrX2WtamTVs+/vh9AG644SYef3w2GzduoGvXS+jffxDNmrmTZDfeeDOLF7/Ixx9/wCWX9OSyy4acd63oykZJqRIwGAy8Oqod2cUoCGbyMWJ3nN+FucVkLNWsMXjPuLNq1Uc8//zTDB06jD59+jFhwl385z93nHFfX9/CM/Gdbsjl+vXrcDgczJv3GPPmPebZzul0snHjBgYNGozJdOZfxbOt+2c8Tq0Nle/Uc9y06UemTbuPyy8fQrdul3Dzzbfx1FNPFulYTZs2Jza2Pt9++zWHDh2iV6/eniKxIiLVgcvlIjXbnpdwshYkn1KzOZqazbG0bBwlHFlvNECAn48nieTv60Ng3s8Av7yXr4kAPyMBfiYCfN0/Pdv5+RDom/fTzweLrw8mo/vfAIMBIiODOXEivRSjUbn5+/szd+5c5s6dW2jd7t27z7jf6dbddttt3HZb5Zt8JDbUPVrr75NZFdwTEZGapST3u6WhvO53hw0bXqr3u8XpQz6Hw4kjLz8waNBgOnW6mG+//ZoffviOhx6ayg03jOa22+7k//5vDP36DWTjxg18//23TJ48nilTZjB06PBi96OyUlKqhAwGA/6+PkXe3l3VvnLnmz/66H1uvnkc//73TQCkp6eTlHTyvJ/t/eqrL+jUqQuTJ9/jWebjY2TKlHv47LPVDBo0mNjY+uzdu8ez3uFwMGrU1Tz00BxiYxuwd+9f9OzZG4DU1BRuuGEkr776Fr6+vmRlFVys5hdAP5NVqz7kiiuu4t57pwLuTHdc3BE6dboY4KzHqlcvmoEDL+f777/lyJHDjB+vR/dEpOrJsTuJT8v2Gu0Ul2r1JJ4yc85em8lsMhIdYiEmzEJMqIWIQD9PoqkguVQ48WQugwtNqd5iw9wzBh5NsZLrcGIyqhSqiEh5Ke79blWQf797001jsNudpXa/ey4NGjRk69ZfvZbt3LmdBg3cj9gvXvwi/foNZPjwkQwfPpK3336DtWtXM3r0Lbz88gvccMNNjBr1f4wa9X/Mn/84X3+9XkkpqZ5CQ0P55ZfN9OzZh6ysLF555UXsdju5uTnn3vkM4uOPsmPHdh555EkaN77Qs9xkMjJs2AgWLVpIYuJxRo68nnvumUi7dh1o06YdK1cux+l00rx5C0aOvJ7nn3+aJk0upGHDC3jlFfdztPXqRdOiRSvWrl3DgAHuRxZee23RWfsTEhLKjh3b2LdvLwaDgXfeeYOTJ094psM+27EABgy4jLfeeh2LxeJ55E9EylaGzc72o2nE7zyO3ZaLv68PQXnJj0A/U97Pgvc+xpqd+HC5XCRl5eYlnax5Sadsz+inxIwcznXpVTvIj5hQS97L35OAigm1UCvQD6OSS1IOIgL9sJiMZNudxKfZqB9W8XWuRESk6sq/37300r6kpWWUyv3uqfbt28tPP/3gtaxly1ZcffW1rFixnMWLX2Tw4CvZufN3PvhgBXffPQWAQ4cO8swz87jnnqkYjUZ++ul7mjZtjtlsZvv2rRw/fow77phAVlYW27b9Rq9el5ZKfysLJaXEY/Lk+3j88dmMGfNvwsPD6d9/IBaLP3/9debHAM5l3bovCAsLo2fPPoXWDRlyFa+9toi1az/lxhvHcM89U3n99Vc5efIELVq0Yt68ZzGbLVx22RASE4/z1FNzyczMoEOHTjzyyDwARo26gf379zJhwm3Url2byZPvY8qUu87Yn7Fjb+fxx2dx++1jCAwMonv3HgwfPpI9e9zneLZjgfu54kaNLqB58xZnfdRPREruRGYOW4+ksjUuld+OpLL3RCbFmXzLP++xr0BPssrH8zk/keVZZ/b5x7YFSa4AP58KS764XC4cLnA6XThdLux5P51OcLhcuHCR7IAdB09yJCXb63G7o6nZ5xxu7+9rdCebQi2nJJzcn+uGmLFUs29GpWoyGAzEhFnYdyKLIylWJaVEROS85N/v3njjKMLCSud+91TvvbeM995b5rXsmWde5OKLuzJv3jO89NJzLF/+DlFRdZk48W6uuOIqAO67bxpPPfUkEyfehsPh4JJLenDXXe76jnPmPMHTT89l3LjR+Pj40K/fAMaMuaVU+ltZGFxlPVatCjlxIp1/RiM3N4eTJ+OJiKiHr2/hZ0GLyv34Xvk+k1uZVdV4OJ1ORo4cyoMPzqZjx86l0mZpxaK0flcr0ql1YfR/ppoRD5fLxZGUbH6LS/Ukog6nFJ5ZMzbMQseGtci25ZJpc5CZYyczr6h2Vo77c25Jix2dRf5jaO4EljthZTEZvRJE7vd5SSSXC4fT/XK68tbnJZQcLvKWF6wveJ+3X14i6nzPxABEBZsLJZxiwixEh1oI9/etVo/SlfXfSn77cmZlFfv7Pt7JN3tPMqX/hVzbvnrNNlRcNeHfhOJQPLwpHgUUC29FiUd1uI8ojqp6L1oWzjcWZ/vdKer1k4Z6iBTRDz98x+bNP+LnZ6Z9+44V3R2RKsnhdLE3MdOdhIpLZWtcGiczvYdMG4CmtQPpEBtK+5hQ2seEUDvYfM4Lqhy7052gyrXnJa4KElYFCayC9/nJrax/rMvIceDIG5qVlesgK9fBicwyDkwx+RggwGxy13bKf8wuL+EUE+pPvRAzvj6qvyNVX2you65UXIq1gnsiIiIiZUFJKZEievfdtzl06G/mzHkCo4qtihSJze7kj4R0z6N424+mFSqk7etjoHXdYHcCKjaUdtEhBJmL/8+Tn8mIn8lIGIVnSikOl8tFjsNVkMCyFSS6snIcZNsdGA0GfIwGjAYDRgP4GA34GAwYPT9xb5P33ueU7Quvd7dhym/PaMDHgNcxfPKX5X3Wt8BSU8TmPbJ3JLXwCEoRERGp+pSUEimiF15YXNFdEKn0Mmx2th1N8zyK90dCOjn/eKwu0M+HttEhnpFQreoGYzZVnkSvwWDAbDJgNvkRHlDRvRGp2WLyZuA7opFSIiIi1ZKSUiIiUmJFKUpeK8DXk4DqEBPKhbUDa/wMeSJSNPkjpeJSsnG5XNWqFpqIiIgoKSUiIkVUnKLk+Qmo9rGh1A+z6EZSREqkXogZowGy7U5OZuYQGWSu6C6JiIhIKVJSSkRECnE4XcSnZXM4xcrBJCvb41L57QxFyS+sHehJQLWPCaG2bhpFpJT4+hiJDvPnSLKVIynZSkqJiIhUM0pKiYjUUE6Xi2PpNg4lWzmcbOVwitXzPi41G/s/n8PDXZS8VVQw7WPdI6HaRocQbNE/JSJSdhpGBLiTUqlW2seGVnR3REREpBTpTkJEpBpzuVwkZuRwOMXK3/nJp2Qrh1KsxKVYCxUhP5Wfj4GYMH8ahPnTqm4w7WNDaBUVjMXXpxzPQERquga1Avmekxw5zePCIiIiUrUpKSUiUsW5XC5OZuV6JZzyRz4dTraSbXeecV+T0UBMqIX64f40CPenfpi/532dILMKkotIhWsY4Z4GMy5VSSkREZHqpvLMwS2l6s47xzF79oOnXffFF59x+eV9ycnJOe16gPj4o/Ts2Zn4+KMA9OzZmV9//eW02/766y/07Nm5yH1bv34dSUlJACxZspiJE28r8r7FZbVaGTCgJ3feOa7MjiFSHlwuF8lZOWyLS2X1zgRe/u4A01b9yf+9/SuXvvADgxf9xG3vbeORL/7izc2HWb/nBHsSM8m2O/ExQP0wC5dcEM71HaK5v18Tnr/mIj685WK+ndyTlWMv5pmrL+LuS5swsn00XRuGUy/EooSUiFQKDWvlJaVSrBXcExERqSwq+/1ucnLZ3++OHDmUTz9dVSZtlyeNlKqmBgy4jFdeeZHc3Fx8fX291q1f/yWXXtoPPz+/Irf38cdrCQk5/zoOCQnxzJz5AB98sJqQEPjXv27k2mtHnXe7Z/Ldd98QERHJ779vIy7uCDExsWV2LJHistmdZNjspNvsZOS90m0O0m12MvOWn8x2sDchjUMpVjJsjjO2ZTRA3RALDfJGOtUP9/e8jw4xY/LRdxAiUjXVz0tK6fE9ERHJV9nvd1es+AQo+/vd6kBJqWqqb98BPPfcAn75ZRPdu/f0LM/MzGDz5p+YP/+5YrUXERFZKv1yubzr1wQEBJRKu2eybt3n9Op1KT//vIm1a9dwyy23l+nxpOZwuVxk5jjykkkOT2IpPe9z4WST93YZNvtZ6zmdjgGICjYXftQuzJ/oUAt+JiWeRKT6yX98L9maS2aOnUA/Xb6KiNR0ut+tPvSvejUVHh5O585d+eabDV5/pN9++w0hIaF06NCJxMTjeX/IP2OzZXPBBY256677adu2faH2evbszPPPL6Jjx85kZmYwb97j/PDDd0RERHLVVcO9tt2+fSsvv/wCf/21C4PBQPv2HXnggZlERkZy7bVXATBixJVMn/4w8fFH+e23LSxc+AoAO3Zs58UXn2PPnt2Eh9fihhtuYvjwkQA89tgsQkJCSExM5PvvNxIaGsZtt93J5ZdfcdoYpKWlsXnzTwwdOhxfX1/Wrv2UsWNvw2AoeCTp888/5c03l3DsWAJNmzbnnnum0KxZCwCWL3+HlSvfIzU1hTZt2nHffdOIjo5h4sTb6NChkyfBFR9/lGuvvYoVKz6hXr1oevbszJgx4/jwwxVcdFFb5s59hlWrPuLdd9/m6NE4AgMD6ddvEHfddR8+Pj5nPNaJE4lMmnQ7H320lvDwcAB27fqTCRPGsWrVFwQEBBb310LOwuF0kZCenVeLKZukzBx3AinHQUa2nYwcO+nZeQmlvGTUaSanKzYDEGj2IdhsIijvFWw2EWz2Ichs4oK6IUT4GYkN8yc21KIi4yJS4wRbfAnz9yXFmsuRlGya1wmq6C6JiEgFO5/73Y4dOxZqr7Tvd6+99qoyv989l7MdKyEhgblzH2HHju2YzRb69x/IpEn3YDKZ2LPnL5566kn27NlNcHAIw4aN4Oabby1RH4pCSamScrnAXozaBi4jnKXYcJGY/MFQ9BovAwYM4sUXn8XhmO5Jfqxfv47+/QdiNBqZM+chgoKCWbz4dZxOJ4sWvcBTTz3Jm28uP2u78+c/waFDB1m48BVSUpJ57LFZnnUZGRlMmXIX119/Aw89NIcTJxJ5/PE5vPPO69x11/28+uqb3HrraJYufZuGDS/gnXfe9Ox78OAB/vOf8Vx//b+ZNu0hdu7cwVNPPUl4eAR9+vQF4P33/8ett47n9tsnsHLle8yf/zg9e/YhKKjwBerGjesxGo107tyVWrUiePvt19m27Tfat3f/T2jTph954ok53HXXfXTu3JWVK5czZcrdrFjxCWvWfMLrr7/KlCkzaNasBYsXv8hDDz3AkiVvFyn233+/kZdfXoLD4eS337bw7LPzmTnzEZo1a8GuXX/wyCMz6dz5Yvr06ceHH6487bFee+0tIiNrs3HjBoYNG5H33+9LunfvqYRUCdmdLhLSsjmSYuVQsvtnfjHwuNRs7CXIMpmMBncSyZKXVPLzcb/3y0swWXwI8jtl/SkJqGCziQA/H4xn+Ls2GCAyMpgTJ9JxlUICTESkqooNs5BizSUuxaqklIhIeSju/W5pKKf73WXL/nfWdkvjfvfVV9+kceMmZXq/ezbnOtazz87D3z+At956lxMnTvLgg1No2PACRoy4lkcffZi2bdszc+YjHDr0Nw8+OIUWLVp6Jf9Kk5JSJeFyEfbB1fgmnL4QWlnJrXcxKVd/UOQ/1D59+jJ//hNs2/YbHTt2JiMjg59//omxY2/D5XLRq9elXHppP+rUiQJgxIjruP/+yWdtMyMjgw0b1vH884to3tw9omjMmHE8/fRcAGy2bEaPHseoUTdgMBiIjo7h0kv78eefOwEICwv3/DSbLV5tr1r1Ic2aNef22ycA0KBBIw4ePMB///uW54/0wgubccMNowEYN+52Vqx4lwMH9tGmTbtCff3yyy+4+OKuWCwWWrZsTZ06UXz22WpPUurjjz9g4MDLPdniCRPuwmTyJS0tlU8++YDrrvs3/fsPAuCee6bw7rvvYLMVrZ7FsGEjaNCgEeAe3fTAAw/Rp08/AOrVi2b58mUcOLA/Lyl1+mPl5Njo338QGzas8ySlNmz4igkT/lOkPtRU+Yknd7LJ/dOdhLJy9ByJJ18fA7Gh7kfiagf55Y1YcieSvEcxFSwzm4xeo+9ERKT0xYZZ2BGfrrpSIiLlQfe7VeJ+92zOdaz4+HiaN29BvXr1qFcvlvnznyM4OASAhISj9OrVh7p16xEdHcOzz75EvXrRxTp+cSgpVVJV4CY0ICCQSy7pyddff0XHjp359tuvqVcvmhYtWgJw9dUjWbfuc3bs2M7ffx9k9+5dOJ1nH811+PDfOBwOmjZt5lnWsmUrz/uIiEgGD76S995bxp49f3Hw4AH27v2rSH9EBw8epFWr1l7L2rRpy8cfv+/5HBtb3/M+MNCdLbbb7YXaOnnyBFu3bmHKlBkAGAwGeve+lE8/Xc3dd0/BYrFw6NDfDB8+wrOPr68vEyfeBcChQ38zdmxLz7patSKYMOHs/wM7Vd26BX+0LVq0xGw2s2TJYg4c2Me+fXs5cuQwXbp0yzvWQcaOLZiR4dRjDRx4Ge+9t4zU1BSOHo0jNTWlzDLUVUl+4ulQsjVvtFP+Y3fnTjz5+RiICXPXYYoN86dBuIXYvNpMdYLMmnFORKQSig31B+BIqmbgExEpF7rf9SyrjPe753usG264iccfn83GjRvo2vUS+vcf5Cljc+ONN7N48Yt8/PEHXHJJTy67bEip1dw6HSWlSsJgcGdwizGc0WQyYi/nx/cABg68nGefnc/dd09h/fovGTDgMgCcTid33z2B9PR0+vcfSI8evcnNzWXGjPuL1O6pBdxMpoLZDhITjzNu3I00b96Szp27ctVVV/PDD9+xc+fv52zzdLMjOBxOHI6CuP1zZoV/9iXf+vXrcDgczJv3GPPmPebZzul0snHjBgYNGozJdOZf/7Ot++eoGIej8Ixop57Lpk0/Mm3afVx++RC6dbuEm2++jaeeerJIx2ratDmxsfX59tuvOXToEL169cZsNp9x++rE7nQRn5rtebzucN6jdkdSsolLzcZxjsRTbFhBIfD6YZa8n/7UCTaf8XE5ERGpnGLD3N82a6SUiEg5KMH9bqnQ/W6R73fP91iDBg2mU6eL+f77jXz33UYeemgqN9wwmttuu5P/+78x9Os3kI0bN/D9998yefJ4pkyZwdChw4vdj6JQUqqkDAbwLUYlfZMRDOeZlCqB7t178MQTs/n111/YsuVn/vOfewE4eHA/W7f+yqpVX3qKaH/wwQrg7L/0DRo0xGQy8eeff9C5cxcA9uzZ7Vm/ceMGgoNDmTfvWc+ylSvf87w/22NODRo0ZOvWX72W7dy5nQYNGhbxbAt89dUXdOrUhcmT7/FaPm3afXz22WoGDRpMbGx99u7d41nncDgYNepqHnpoDrGxDdi79y969uwNQGpqCjfcMJJXX30LX19fsrKyPPsdPRp31r6sWvUhV1xxFffeOxVwZ7rj4o7QqdPFANSvf+Zj1asXzcCBl/P9999y5Mhhxo+vno/uOV0u/jyWwff7T7Inycq+Y+kcTbOdNfFkNhmJCbXQINzfM9KpfphFiScRkWooNsw9UiouVUkpEZFyUdz73QpSU+93z+Vcx1q8+EX69RvIiBEjueqqEbz99husXbua0aNv4eWXX+CGG25i1Kj/Y9So/2P+/Mf5+uv1SkpJyfj5+dG7d18WLnyGxo0vpH79BgAEBQVjNBr56qvP6dmzD3/+uZOlSxcDkJOTc8b2AgODuPzyK3j22flMm/YwNls2S5e+4lkfEhLKsWMJ/PLLZurVi2bDhnV88816WrRwD3m0WNwXlXv3/kVQUIhX21dffS0rVixn8eIXGTz4Snbu/J0PPljB3XdPKdY5x8cfZceO7TzyyJM0bnyh17phw0awaNFCEhOPM3Lk9dxzz0TatetAmzbtWLlyOU6nk+bNWzBy5PU8//zTNGlyIQ0bXsArr7ifo3UPB23F2rVrGDDAXQPqtdcWnbU/ISGh7NixjX379mIwGHjnnTc4efKEJ87XXTeKZ5556rTHAhgw4DLeeut1LBaL55G/6iA718HmQyls3HeS7/cncSKz8O+d2WQkNi/RVDDqyZ/YMIsSTyIiNUj+SKljadnYHU5MPsYK7pGIiFQGlfl+NzQ0zKvt0rrfPdW+fXv56acfvJa1bNnqnMc6dOggzzwzj/vvfwCXy8BPP31P06bNMZvNbN++lePHj3HHHRPIyspi27bf6NXr0hL38VyUlKoBBg68jE8/XcWkSXd7ltWpE8W99z7AG2+8xuLFL1K/fkMmT76PRx99mD17dp/1mdG7776fZ56Zz913TyA4OJiRI0fx4ovPAtCv30C2bfuNBx+cisFgoGXLVkyceBdLliwmJyeHsLAwLrtsMDNmTGX8+Ele7datW5d5857hpZeeY/nyd4iKqsvEiXdzxRVXFet81637grCwMHr27FNo3ZAhV/Haa4tYu/ZTbrxxDPfcM5XXX3+VkydP0KJFK+bNexaz2cJllw0hMfE4Tz01l8zMDDp06MQjj8wDYNSoG9i/fy8TJtxG7dq1mTz5PqZMueuM/Rk79nYef3wWt98+hsDAILp378Hw4SM9GffLL7+ChIRjpz0WuJ8rbtToApo3b3HWR/2qgsQMG9/uT+LbfSf5+VAKtlMeaQ3w9aFbo3D6ta5LpJ+R2DB3sXElnkREJCLQD7PJiM3uJD7NRv1w/4rukoiIVBLFvd/9669dhIVFnLG90rjfnTlzWpnd757qvfeW8d57y7yWPfPMi1x8cdezHuu++6bx1FNPcuedt2K3O7jkkh7cdZf70cY5c57g6afnMm7caHx8fOjXbwBjxtxS4j6ei8FVkgcUq6nTTbuem5vDyZPxRETUw9e38HOZRVUqNaWqEcWjwLli4XQ6GTlyKA8+OJuOHTufcbvS+l0tTS6Xi93HM/h2XxLf7j/Jn8cyvNbXCzHTq3EEvZrUomNsGGZfI5GRwaf9W6yJDAYUjzyKhTfFw1tZxyO/fTmzso79iRPpXPf6L+w/mcXz11xE90a1Sv9glZz+7r0pHt4UjwKKhbeixKMy3keUJd2LFjjfWJztd6eo109Ve9iFSDX3ww/fsXnzj/j5mWnfvmNFd6dIbHYnv+Q9lvfd/pMczygYHmsAWtcLpneTCHo1jqBJZMBZn7sWERHJFxvmz/6TWSp2LiIiUo0oKSVSib377tscOvQ3c+Y8gdFYeetnnMjM4fv9J/l2XxKb/k4m+5Rsu8VkpFujcHo1jqBH41pEBFb/b19ERKT0FczAV86zQYmIiEiZUVJKpBJ74YXFFd2F03K5XOxJzOTbvETUzoR0r/V1gvzo1SSCXk0i6Fw/DLOp8ibURESkaogJzZuBTyOlREREqg0lpUSkSHLsTrYcSXHXh9p3koR0m9f6llFB9GoSQe8mETSrHajH8kREpFR5RkqlaqSUiIhIdaGklIicUXJWDt/tT+Lb/UlsOphMVq7Ds85sMtKlQRi9mkTQs3EtageZK7CnIiJS3cWGFYyUcrlc+vJDRESkGlBSqohcLlXnl8qtNH5HXS4X+09msXGf+7G8HfFpnDpJR2SgHz0b16J3kwgubhCGxdfnvI8pIiJSFPVCzBgNkG13cjIrl0jVKBQRKVW655XiKo3fGSWlzsFk8sVgMJKaepKgoDB8fEwl+mbO6TTgcGhO0nyKR4HzjYXL5cLhsJOenoLBYMRk8i36sV0ujqRksycxg9+OpPLt/iSOpnrX6mheJ4hejWvRq0kELaKCMOqbaRERqQC+Pkaigs3Ep9mIS7EqKSUiUkpK6563qtC9aIGSxuJ87kH/SUmpczAYDERE1CU1NYnU1BMlbsdoNOJ0KvOcT/EoUFqx8POzEBJS64z/gGTm2NmbmMlfiZnsScxgb2Ime09kYs31Prafj4GLG4TTq0ktejaOICpYj+WJiEjlEBPmT3yajSMp2bSLCa3o7oiIVAuldc9bVehetMD5xuJc96BFoaRUEZhMvtSqVQen01Gi/2AGA4SHB5KcnIlLCVnF4xSlFQuj0YjR6IPBYMDlcnE0LZs9xzPZk5jJX4kZ7EnMJC719LMVmU1GGkcE0CIqiEsa1aJro3D89VieiIhUQrGhFn4BjqSo2LmISGk633veqkL3ogXONxan3oOeDyWlishgMODjY8KnBPfqBgNYLBZ8fXNr/C8+KB6nOt9YZOc62Hcif/STewTUnsRMMnMcp92+TpAfF9YOpGntIJrl/awf7o/JWH2H54qISPWRX+z8yBm+aBERkZI7n3veqkL3ogUqSywqNClls9mYPXs2X3zxBRaLhbFjxzJ27NjTbvvdd98xb948Dh8+TLt27Zg5cyaNGzcG3M8zLly4kBUrVmC1WunRowczZ86kVq1a5Xk6ImXG5XJxLN2Wl3hyJ5/+SszkcLKV0/3/w9fHwAW1AmhaJz/5FEjTyCDCAkr+rK+IiEhFiw2zABCnkVIiIiLVQoUmpebNm8eOHTt48803OXr0KFOnTiU6OprLL7/ca7s9e/Zw++23c9tttzF06FBWrlzJ6NGjWbt2LYGBgbz33nusXLmSBQsWEBYWxqxZs5gxYwYvv/xyBZ2ZSMnZ7E4OnCw8+ikt237a7WsF+LqTTrWDaFo7kGa1g2hUyx+Tj7Gcey4iIlK2YkPzRkqlaKSUiIhIdVBhSamsrCxWrFjBq6++SuvWrWndujV79uxh2bJlhZJS7777Lh06dGDy5MkA3H///Xz99desWrWKUaNG8c033zBkyBC6dOkCwLhx47j33nvL/ZxEistmd/LNX4n8vCfRM/rpUFIWp5sAwcdooFEt/1MevXMnoiI0+5CISI1TnNHmn3zyCS+++CLx8fG0atWK6dOn07ZtW8/6zp07k56e7rXPr7/+SmBgYJmeQ0nE5I2USrbmkpljJ9BPlShERESqsgr7l3zXrl3Y7XY6dOjgWdapUycWLVqE0+nEaCwY5XH48GGviyeDwUCzZs3YunUro0aNIiwsjK+//poxY8YQGhrKmjVraNmyZbmej0hxxKVa+WBbAp/sSCDFmltofajFRNM6QTSNDPSMfrogIgA/k0Y/iUglkl+AoBpPG11ZFXW0+S+//MKMGTN49NFH6dixI//973+59dZbWb9+PYGBgRw7doz09HTWrVuHxWLx7BcQEFDep1QkQWYTYf6+pFhziUvJplmdoIrukoiIiJyHCktKJSYmEh4ejp9fwSiPyMhIbDYbKSkpXvWgIiMjOXbsmNf+CQkJhIa6pwKeMGEC48ePp3fv3vj4+FC7dm3ee++98jkRkSJyulz8eDCZlVuP8v3+JE8tqLohFtrUC/Ykn5rWDqR2kN95z2IgIjWEywVOOwaHDezZGBy2gvd5nwstd9gw2G0Y7NngyP+cDfnLHdlgt51leUHbLpM/jqBonEHR7p/Bp7zP+4lf5RtxU5UVZ7R5YmIid955J8OGDQPc10xLly5l3759tG3bln379lG7dm3q169fEadSIjGhFlKsuRxJVVJKRESkqquwpJTVavVKSAGezzk5OV7LBw8ezJ133smVV15Jr169WLVqFb///jtdu3YFIC4uDovFwqJFiwgJCWHevHlMnz6dpUuXFqtPZZUDyG9XOQa3mhaPFGsun/yewPvb4ok7Zbagbo3CubZ9PYZd3JDUFE1JCjXvd+NcFI8CZRYLpwNDbmbBKycTQ24GhtwsDDkZkJuJMTcTPOsyMdit4HKCy4kBl/u90wE4weXCkLfO/XLlLc/b3mtdwTYG/rGPywkuR972eZ+92nGBy06EPdudZHJV3NTNBrsVU8o+SNl3xm2c5lCc+UmqvKSVM6gejuCYvPd1wcdc8j6U8d9KZfsbLM5o88GDB3veZ2dn88YbbxAREUGTJk0A2Lt3LxdccEH5db4UxIZZ2JmQrmLnIiIi1UCFJaXMZnOh5FP+51OHjwP07t2bCRMmMGnSJBwOB127dmXYsGFkZGTgcrmYOnUqU6ZMoW/fvgA8++yz9O3bl23bttGuXbsi9ykiIvg8z6pi269qqns8th1O4a0f/2bV9qPk2N03jCEWE9d2rs//dWvIBZEFIweqeyyKS/HwpnjkcbmICDFBTibY0t0/czLcL1tG3udMyMlb51l2ls/2qn1Te9pciY8ZTBbwtYAp773nZQZf/2IsP8c+Jos7/qlHIC0OUuMg7Yj7Z/4yWxpGWypGWyqc/PPMJxNYB0JjICQGQuuf8j7W/TO4LhjPPkd1TflbKc5o83w//vgjY8eOxeVysWDBAk+9qH379mG1Wrnxxhs5cOAALVu2ZPr06ZU6URUTpmLnIiIi1UWFJaWioqJITk7GbrdjMrm7kZiYiMViISQkpND248eP55ZbbiE9PZ2IiAgmT55MTEwMSUlJxMfH07x5c8+29erVIzw8nLi4uGIlpU6eTC+T0SoGg/tCuazar2qqczyycx18sSuRlVuP8sexDM/yFlFBXNs+msta1Mbi6wM4OXEivVrHoiQMjmwiQnw5meWjeFC9/1Y8nHYM1iSM1hMYsxLzXicwWN0/jVknMOa/z04C5+lnoTxfLoMPLr8gXL6B7pdf3k/fIFy+AXnrAnD5BoHJH5fRJ2/4jBEMBS+XwQAGn1OWubdxnfrZ894nb/k/2zGcsv0pr1PaMRgNhIWHkJzhwOljAR8zLpPZnZAylFPtOUfeiwAIqQMhHSG28GaGnHSM6UcxZrhfPqd5b3DYIPO4+3X0t9MezmXwwRlUt2DEVVA99/vgaJzBMYQ178rJ5Kwy/Xe8sijOaPN8TZs25YMPPmDDhg088MADxMbG0r59e/bv309qair33HMPQUFBvPrqq4wZM4Y1a9YQFFT0R+PKc7R5/bxi50dSrZVuFFtZ0uhZb4qHN8WjgGLhTfHwpngUqCwjzSssKdWyZUtMJhNbt26lc+fOAGzZsoU2bdp4DTsHWL16Ndu2bWPGjBlERESQnZ3Npk2bePLJJwkNDcXPz499+/Z5hqInJSWRkpJCbOxpro7PwuWiTG/8yrr9qqY6xeNwspX3t8WzamcCadnum2Y/HwMDm9dmZPtoWtcN9tSIOt05V6dYlIjTjuXP5QRuWgC2VAKbjyCr/R04ajWt6J5VClXu98OR604y5SWaDKcmlrISCxJQ1hMYrEnuR+CKyWWy5CWM/plECsTplUjKX1+QVDpd0smdzKk6VycGAxAZjMN4moRlJftdcfkG46zVHGo1P8MGLgzZyfhkxHmSVz4Zp/xMP4oxMwGDy4FPehw+6XGnb6f11bj6vlC1/lZKqDijzfNFRkYSGRlJy5Yt2bZtG8uXL6d9+/YsWbKE3Nxcz8ipBQsW0KdPHzZs2MDQoUOL3KfyHG3euqF7gpD4dBuRkZUnWVheKlOCtDJQPLwpHgUUC2+KhzfFo0BFx6LCklL+/v4MHz6cWbNm8fjjj3P8+HGWLl3KE088AbhHTQUHB2OxWGjUqBHTpk3j4osvplmzZsyfP5969erRu3dvjEYjI0aMYO7cuYSHhxMaGsrcuXNp164dbdq0qajTkxrA4XTx/YEkVmw9yk8Hkz3Lo0PMXNMumqsuqktYgG8F9rBq8D30DUHfz8GUtNuzzPLne1j+fA/bBZeR1fFO7HU7VWAPK44xeT9YczClWXHljaRx/yw8Gsd1ymiaU0fkuDzbFYzQcXH6UTtnTcrYs71HLeX9NPwjyWTMSnQ/plUMLgy4/Gvh9I/EGVC74GdAJE7/2rj8I3AG1iY8piEnMww4TYHnfIxLqhCD+7+/3b8W1D7Dv9tOB8as43kjrOLxSY/zSl4Zs47jU7tF+fa7AhVntPn27dvx8fGhdevWnmVNmjRh3z53DTA/Pz+vUVdms5nY2NhCE8ycS3mONg82uB+JP5psJeFYKiafmjEzbY0YPVsMioc3xaOAYuFN8fCmeBQo61gUdaR5hSWlAKZNm8asWbMYPXo0QUFBTJo0iUGDBgHQs2dPnnjiCUaMGMFFF13ErFmzePLJJ0lJSaF79+4sXrzYM6Jq+vTpPPvss9x7773YbDYuueQS5s+fr9nLpEwkZ+Xw8e8JfLA9nvg0G+Cu63LJBbUY2b4e3RvVwseo371z8Un6i8DvH8F8aAPgLoSc1eUegi7siu2bZzDv/xzzAfcrt14XsjreSU7DfuX3aFJFybVi3rsK/51v43vM/RhTWDke3ju5ZfDE21DM2ksug09ecikSV15yyen5GZH3052AcvnXAuPZ/zkyGIDQYFy56ZVuJJCUA6MPzqB6OIPqnXa1wYB7xMyJ9HLuWMUozmjzlStXEhcXx5IlSzzLdu7cSatWrXC5XAwcOJA777yTESNGAO6Z/f7++28aN25crD6V52jziAA/zCYjNruTo6k26of7l92BK6EqN3q2jCke3hSPAoqFN8XDm+JRoKJjUaFJKX9/f+bOncvcuXMLrdu9e7fX52uuuYZrrrnmtO2YzWamTp3K1KlTy6SfIi6Xi9/j01mx9Shf/ZVIrsP9VxtqMXHVRXUZ0a4esWE166K4pAxZJwjc/BSWP/6LweXAZfTF2mYMWZ3/A/7hBEUGkz7kIjKT9uL/28tYdn+Ab/xmQtdsxl6rOVkdxmNrOgx8qtcoNJ+kPVh2voNl90rPSCOX0RdDSDQOh8N7prZ/zMSWPzubweXgnzO+FXdWNs/scKfhMvq5E0ue0UyRuDzJJu9RTi5LWPVPIIpUkOKMNr/++uu57rrrePPNN+nTpw+ffPIJ27dvZ968eRgMBi699FJeeOEFYmJiqFWrFs899xx169alT58+FXyWZ2YwGIgJtbD/ZBZxqdYal5QSERGpTio0KSVS2VlzHaz98zgrtx7lr8RMz/LWdYO5tn00/ZtF5hUul3OyZ+O/fQkBv7yAMdddBN7W+HIyu0/HEeb+Rv7U8WWO8AvJ6PcUWV3uw3/ba1h2LsOUtJuQr+7CsWke1na3Ym31b/ALPM3BqghHDub9a7HsfBu/uB8LFgfXx9r6BmytRhHR4AKST5znkNq8pBVeyay85Napiay8bQw4Cy1zWcJw+YVUqbpLItVZUUebt27dmoULF/L000/z1FNP0bRpU5YsWUJUVBQA999/PyaTiXvvvZeMjAy6devGK6+8go9P5f63LTbMn/0nszQDn4iISBVncLk0aC3fifO98TuD/McKyqr9qqYqxONgUhbvb4tn9c4EMmwOAMwmI5e1qM017aJpVbd0isFVhVicN5cL895VBP74OD7pRwDIrd2WzJ4zyY3u5rXp2eJhsKVi2fE2AduWYLQmAu5H/qxtxmBtczOugMhyOZ3SYEw7hP/OZVj+fA+j9QTgfmwup9FArK3/j9wGfdwzrNWE348iUiy8KR7eyjoenscD5YzK+xrq6Q37ePfXOG7oFMtdlxbvUcOqSn/33hQPb4pHAcXCm+LhTfEoUFmunzRSSiSP3eni230nWbn1KJsPpXiW1w+zcE27aK5sHUWof/V6ZKysmRK2EPTdbHyP/QqAI7Aumd0fwNZsRLEf7XKZQ7F2moi13Tgsu1fi/9siTKkHCfzlOQJ+W0R2y1Fktb8NZ2jDsjiV8+e04/f3eiw73sbv0NeeGeccAVFkt/oX2a3+jTM4uoI7KSJSNcSGuWcZjEstXr07ERERqVyUlJIa70RmDh9tj+fD7fEcz3BPqW00QM/GEYxsX4+uDcMx6pGlYjGmHSbwxyew7P0EAJcpgKyOd5LV/nbwPc/aHyYL2a3/j+yW/8Jv/2cE/PYyvse34b/jTSw738bW5EqsHe/EXvuiUjiT82fMTMDyx7tY/vgvPhnxnuU59XtjvehGchoOqHb1sUREylpMXh1HPb4nIiJStSkpJTWSy+Via1waK7YeZf2eEzic7lEr4f6+DGvjLlxeL8RSwb2segy2NAJ+XYj/tiUYHDZcGMhueT1ZXe/HGRhVugcz+pBz4ZXkNLkC37gfCPjtJfwOfYNl7ydY9n5CTv3eZHW4k9zYHuVfB8nlxPfId/jveBu/A1+4C5ADTks42S2vx9rqBpxhF5Rvn0REqpHY0IKRUi6XSzMui4iIVFFKSkmNcyIzh7nr9vD13pOeZW2jQ7i2fTT9mkbiZ9KMYcXmtGP5410CNy/AaHXHNSemBxk9H8YR2apsj20wkBvbg9TYHvgk7iTgt5cw712F3+GN+B3eSG7ttlg7jMfWZAgYy7Zwr8GahGXX/7DsfAdT6kHP8tx6XbBedCO2xoPBpGSniMj5ig61YDSANdfJyaxcIgP9KrpLIiIiUgJKSkmN4XK5+OzP4zy1YR9p2XZ8jAaGto5iZPtomtcJqujuVVl+f68n8PtHMSX/BYA9rAmZPR4ip2H/ch+h5KjdmvRBL5LZbSoBW1/B8udyfBO34/vFeBwhDcnqcAfZLUaCqRSnD3e5MMX/jP/OtzHvXYPB6X4E1OkXjK35NVhb/x+OiBaldzwREcHXx0hUsJn4NBtxKVYlpURERKooJaWkRjiebuOJdXv4bn8SAM3rBDHzsmY0q4hklMuJT9IefI/+hN/RnyD9IEGhF5JbtzO59S7GUat5mY/oKQ0+J/8k6PtH8Tv8DeB+NC2zy71kt7qhwmskOUMakNH7UTIvvhv/7a/j//sb+KT9TfA30wjc/BTWtrdgvehGXJawEh/DYEvD/NcH+O94G1PSbs/y3NptyL7oRrKbDgffgPM/GREROa2YMH/i02wcScmmXUxoRXdHRERESkBJKanWXC4Xq3Yc45lv9pFhc+DrY2Bct4bcdHEsJp9yekzP6cB08k98j/6U99qEMTvZaxPLsd+x/PWhe3PfQOxRHcmt15ncup2x1+2Iy6/yTEVuyEokcNMCLH++i8HlxGX0xdp2LFmd/4PLXLluClz+EWR1vY+sDuPx//Nd/Le+ik9GHIGb5uL/60KyW92Atf04nEFFn/XOlPg7lh1vYfnrIwx296xPLpOF7KbDyb7oRux12pXV6YiIyCliQy38gmbgExERqcqUlJJqKyEtm8e+3MNPB90JoFZ1g5l5WTOaRAaW7YEduZgSf/ckoHzjf8aYk+a1icvk7x4ZFdOVwEbtyTzwK77xv2BK+BVjbgZ+R77F78i37m0x4IhokTeSyp2ocoY0KP/i3XYr/tuWELDlBYy5mQDYmlxBRvdpOEMblW9fissvEGu7cVgvGo157ycE/PoSpqTdBGx7Bf/fX8fW7GqyOtyBo1az0++fa8Wy52MsO9/G9/g2z2J7eDOsF/0ftubXVLqEnIhIdReTV+xcM/CJiIhUXUpKSbXjcrn4cHs8z288QGaOAz8fA3f0aMS/OsViMpZBIsdhw/fYVncC6uhP+Mb/gsGe5bWJ0zeI3HoXkxvTjdzobthrtwEfPwwGCIwMxlr7UrJcgNOBT9JufBN+wTf+F3wTfsEn7RCmk39iOvkn/jvfdh8yoA72up08iSp77YvAx1z65wbgcmLe8zGBPz6JT0YcALl12pHR42Hs0V3K5phlxccXW/NrsDUbgd/f6/H/7SX8jm5yFyff9T9sjQaS1fFO7PUudm+e9BeWne9g2bXSk1h0GX2xNRlC9kU3kVuvS/knB0VEBIDYMHd9QCWlREREqi4lpaRaiUu18ugXe/jlUArgnlXvocua0ahWKdb2ybXim7Cl4HG8Y79hcNi8NnGaw8iN7kpudDdyo7tij2wFxiL8uRl9cES2whHZiuyLbnIvyjyGKeEXfOO34JvwC6bE3/HJOo7P/s8w7/8MAJePGXudtu4kVV6iyuUfcd6naor/maDvZuN7fCsAjqBoMrtPw9Z0GBiq8CyFBgM5jfqT06g/poQtBPz2Mn77P8d88EvMB78kt97FuAxG/I5u8uziCGmItfUNZLe4DldAZAV2XkREAGLD3COl9PieiIhI1aWklFQLTpeLlVuPsvDbA1hznZhNRu7s2YjrO8Tgc56joww56Zjif8Ev73E80/FtGJy53sf3jyQnLwGVG9PNXay8lJI2zsAocppcQU6TK9wL7FZMx3/HN+FnT6LKmJ2Eb/zP+Mb/7NnPHnoB9rzH/XLrdsZRq2mR+2RM/ZvAH5/Asm+1uw++gVg7TiSr/bjSnbmuErDX7UTa4NfwSd6H/28vY9n9vieOLoMPOY0GYL3oJnLr96raiTgRkWomf6RUUlYumTl2Av10WSsiIlLV6F9vqfIOJVt59PPd/BbnfryqY2woD13WzHOxWlyG7BR84zfjG/cTvvGbMCX+jsHl9NrGEVTPMwoqN7o7jrDG5fcYl8kfe3QX7NFdsAK4XPikHsAU/4snUWVK/gtT6gFMqQew7FoBgNMcSm5Ux4JEVVSHQrPDGWypBPzyPP7bX8fgzMFlMJLd8l9kdrkXV2Cd8jm/CuIIb0JGvwVkdb0Py5/v4TL4YGs+olhF0EVEpPwEmU2EWkykZtuJS8mumBl1RURE5LwoKSVVlsPpYvmvcbz8/UFsdif+vkYm9W7MNe3qYSxGgsiQdcJTlNzv6E/4nNyFAZf3sUIakhvd1TMaqkIKjZ+JwYAjrDGOsMbYWl7nXpSdgm/CFkwJ7pFUvsd+w2hLxXxoA+ZDGwD3KCB7ZGty63bCXq8zBmsSgT8/7ZkZMKd+bzJ6PIQjomWFnVpFcAbWJavz5IruhoiIFEFsmD+pCekcSVVSSkREpCpSUkrKlc+JPwj+9iGwHifcCS6D0f1IVN7L6zOGf6zL/+yDNdfF3pNW2tkcvGQwEhriR9M6wVjiTZBgxGXwOXsbGDE4czAd+w1T8t5C/bSHX0huPfejeLn1uuIMrlqjZVyWME/NJMA9I+DJP/GN/zkvUfUzPhnx+CZuxzdxO/z+umdfe3hTMns8RE6DvpUn8SYiInIasWEWdiakE5eiulIiIiJVkZJSUm789n9OyJeTPDPT+ZxPW0CnUxvJAY6UvD17RAtyo7t5RkK5AmqfR+8qIR9f7HXaYq/TFtrdAoAx/ai7cHr8z/gmbMGQm4G13TiyW/27aEXZRUREKlhM3qP6camagU9ERKQq0p2nlD2XC/9fXyTwp7kYcJET2wO/gQ+SkmoFl9P9cjoB93uDywkuV8G6vOUJaVY++C2O+FQrBpw0qx3AVa3rEGrxweBy5G1fsJ+7DtQp7XiWu9xtAvZaLciN7oLLEl6BAaoYzuBobMFXYWt6VUV3RUREpERiQt0z8B3RSCkREZEqSUkpKVv2bII3TMHy1wcAWC+6icxes4mMqoX9RDou1zn2B+wOJ29sPsySnw5hd9Yh2Gzi7ksbc2XrKAwGA7YyPgURERGpnGLD8pNSGiklIiJSFSkpJWXGkHmc0M/G4XvsV1wGHzJ6zSG7zehilSnafTyDOWt381diJgC9Gtdi2sCm1A4yl1GvRUREpKqIDXU/vpeQlo3d4cTkY6zgHomIiEhxKCklZcKUuIOQT8fik3EUpzmUtMsWkVu/V5H3z3U4WfLTId7YfBiH00WoxcR9/S7ksha1Maj4toiIiACRQX6YTUZsdicJ6TZi82pMiYiISNWgpJSUOr99nxKybjIGuxV7WBPSrngdR1jjIu//R0I6cz7fzb4T7oLo/ZpGMqX/hUQE+pVVl0VERKQKMhoMRIdaOHAyiyMpViWlREREqhglpaT0uFwEbHmewE3zAcip34e0y17CZQ4t0u42u5NXfvibd345jNMF4f6+TB1wIf2bVbOZ8ERERKTUxHqSUqorJSIiUtUoKSWlw24leP19WPZ8DEBW27Fk9pgJxqL9im0/msYjn+/mYJJ79pzLWtTmvr4XEhbgW2ZdFhERkaovf3SUklIiIiJVj5JSct6MmQmEfHoLvse34TKayOj9KNmt/69I+2bnOnj5+4O8uyUOFxAR6Me0ARfS58LIsu20iIiIVAv5M/DFpVoruCciIiJSXEpKyXkxHd/mLmieeQynOYy0wa+QG3NJkfb97Ugqc9bu5nDeN5tXtKrD3Zc2IdRfo6NERESkaGLyRkrFpWqklIiISFWjpJSUmHnPKoLX343Bno09vCmpV7yOM7TROfez5jiY9clO3vzhIC6gTpAf0wY2pWfjiDLvs4iIiFQvMaHukVJHUqy4XC7N0isiIlKFKCklxedyEvDzMwT+/AwAtgZ9SR/0Ii5zyDl3TUjL5o7/bfd8mznsorrcdWljgsz6VRQREZHiiw6xYACsuU6SsnI1W6+IiEgVokyAFE+uleCv7saybzUAWe1uI/OSGWD0OeeuNruTKZ/8QVxqNjFh/kwb0ISuDWuVdY9FRESkGvMzGYkKNpOQbuNIilVJKRERkSpESSkpMmPGUXdB88TfcRl9yejzBNmtRhV5/6c37OPPYxmEWky8d3s3LA4HLlcZdlhERERqhNgwCwnpNuJSs2kXE1rR3REREZEiMlZ0B6RqMCX8StiKK/FN/B2npRapw5YXKyG1emcCH2yPxwA8ekULYsMDyq6zIiIiUqPkFzs/kqIZ+ERERKoSjZSSczL/9SHB6+/D4LBhr9XcXdA8pEGR9999PIMn1+0F4NZLGtL9Aj2yJyIiIqUn1lPsXDPwiYiIVCVKSsmZuZwEbFpA4JbnAbA1Gkj6wBdw+QUVuYm07FymfvIHNruTHhfU4pZuRU9miYiIiBRFrGeklJJSIiIiVYmSUnJ6OZmEfDUZ8/61AGR1GE9mtweKVNA8n9Pl4uHPdhOXmk10iJnZg5tj1DTNIiIiUspiw9wjpeJS9fieiIhIVaKklBRiTI8jdM3NmE7+gcvoR3rfedhajCx2O29sOsx3+5Pw8zEw96pWhPr7lkFvRUREpKaLCXWPlErKyiUrx0GAX9G/RBMREZGKo0Ln4sWUsIXwFVdgOvkHTv9IUob/r0QJqU0Hk1n0/UEApvZvSouo4FLuqYiIiIhbsMVEqMX9XatGS4mIiFQdSkqJh3n3SsI+vBaj9QT2iFYkj1yNvV7nYreTkJbNjDV/4gKGtanLVW3qln5nRURERE4Ro7pSIiIiVY6SUgJOB4E/Pk7IurswOHOwXXAZySM+xBkSW+ymcuxOpq76k9RsOy2jgri/34Vl0GERERERbwUz8GmklIiISFWhmlI1nCEng+AvJ2E++CUAmZ3+Q1bX+8BQsnzl01/v44+EdEItJp4c2gqzSXlPERERKXsFxc41UkpERKSqUFKqBjOmHSb005sxndyFy8dMer8F2JpdXeL21uw8xvvb4jEAc4a0IDrvG0sRERGRslbw+J5GSomIiFQVGsZSQ5mObs4raL4LR0AdUoavOK+E1F/HM3hi3R4Abu3ekEsuqFVaXRUREZF/sNlsTJ8+nc6dO9OzZ0+WLl16xm0/+eQTLrvsMtq2bcuoUaPYvn271/rVq1czYMAA2rVrx4QJE0hKSirr7peJ/JFSqiklIiJSdSgpVQOZ/3yPsI+vx5idRG7tNqRcuxp73Y4lbi89287UVX9gszu55IJwbuneoBR7KyIiIv80b948duzYwZtvvsnDDz/MwoULWbt2baHtfvnlF2bMmMGdd97JmjVr6NChA7feeiuZmZkAbN++nRkzZjBx4kTee+890tLSmDZtWnmfTqmIDXWPlEpIy8bucFZwb0RERKQolJSqSZwOAr+bQ8j6ezE4c7E1uYKUq9/HGRRd8iZdLh7+bBdHUrKJDjEzZ3ALjAZDKXZaRERETpWVlcWKFSuYMWMGrVu3ZuDAgYwbN45ly5YV2jYxMZE777yTYcOGUb9+fSZMmEBKSgr79u0D4J133mHw4MEMHz6cFi1aMG/ePL755hsOHz5c3qd13iKD/DCbjDhckJBuq+juiIiISBEoKVVTOB2EfDaOgG2vAJB58d2kXfYy+AacV7Nvbj7Mt/uT8PMxMPeqVoT6+5ZGb0VEROQMdu3ahd1up0OHDp5lnTp1Ytu2bTid3iOEBg8ezPjx4wHIzs7mjTfeICIigiZNmgCwbds2Onfu7Nm+Xr16REdHs23btnI4k9JlNBiIDskrdq5H+ERERKoEFTqvISw738F88Et3QfP+z2JrOvS829z0dzKLvj8IwJT+F9IiKvi82xQREZGzS0xMJDw8HD8/P8+yyMhIbDYbKSkp1KpVuK7jjz/+yNixY3G5XCxYsIDAwEAAjh8/Tp06dby2jYiIICEhoVh9KqtB0vntFrX92DALB5KyOJJqpZshvGw6VUGKG4vqTvHwpngUUCy8KR7eFI8CZR2LorarpFQNYLCeJHDTPAAyeswslYRUQlo2D67ZhdMFwy6qy7A29c67TRERETk3q9XqlZACPJ9zcnJOu0/Tpk354IMP2LBhAw888ACxsbG0b9+e7Ozs07Z1pnbOJCKibL+YKmr7F9YL4dv9SSTlOImMrJ5flpV1rKsaxcOb4lFAsfCmeHhTPApUdCyUlKoBAn+ai9GWij2iFdmt/++828uxO3lg1Z+kWHNpXieI+/o1KYVeioiISFGYzeZCSaP8zxaL5bT7REZGEhkZScuWLdm2bRvLly+nffv2Z2zL39+/WH06eTIdl6tYuxSJweC+WC5q+xFmHwD2xKdy4kR66XeoAhU3FtWd4uFN8SigWHhTPLwpHgXKOhb57Z+LklLVnOn4Nix/vAtAeu9Hwehz3m0+/fU+diakE2IxMfeqllh8z79NERERKZqoqCiSk5Ox2+2YTO5LucTERCwWCyEhIV7bbt++HR8fH1q3bu1Z1qRJE0+h86ioKE6cOOG1z4kTJ6hdu3ax+uRyUaYX90VtP38GviMp2dX2ZqOsY13VKB7eFI8CioU3xcOb4lGgomOhQufVmctJ0MYHMeAiu9kI7NFdzrvJT/84xvvb4jEAcwa3ICa0eN+kioiIyPlp2bIlJpOJrVu3epZt2bKFNm3aYDR6X9qtXLmSp59+2mvZzp07ady4MQDt2rVjy5YtnnXx8fHEx8fTrl27sjuBMhQT5h4pdiTFikt3GyIiIpWeklLVmHnXCnyP/YbTN5DMS2acd3t7EjN4/Ms9ANzSrQE9GhcupCoiIiJly9/fn+HDhzNr1iy2b9/OunXrWLp0KTfddBPgHjWVne2efe7666/np59+4s033+TgwYM8//zzbN++nTFjxgDwr3/9i48//pgVK1awa9cupkyZwqWXXkr9+vUr6vTOS3SIBQNgzXWSlJVb0d0RERGRc1BSqpoy2FIJ+vFxALIuvgdnYNR5tZeebWfKJ39gszvp1iiccd0blkY3RUREpASmTZtG69atGT16NLNnz2bSpEkMGjQIgJ49e/Lpp58C0Lp1axYuXMjKlSu56qqr+Oabb1iyZAlRUe7rgg4dOjBnzhxefPFF/vWvfxEaGsoTTzxRYed1vvxMRqKCzQDEpWZXcG9ERETkXFRTqpoK2PwURutJ7OFNsbYde15tOV0uZq3dzZGUbOqFmHlkSAt8jJpDU0REpKL4+/szd+5c5s6dW2jd7t27vT737duXvn37nrGtESNGMGLEiFLvY0WJCbOQkG7jSIqVttEh595BREREKoxGSlVDPif+wP/3NwDI6DUHfHzPq703Nx9m476T+PoYeHJoK8L8z689ERERkbKSX+w8LkUjpURERCo7JaWqG5eLoG8fwuByYmtyBbn1e51Xc5v/TmbR9wcBuL/fhbSqe+4pHUVEREQqiqfYeaq1gnsiIiIi51KhSSmbzcb06dPp3LkzPXv2ZOnSpWfc9rvvvuOqq66iQ4cOjBkzhv3793utX7t2LZdddhnt27dn7NixxMXFlXX3KyXzno/xO7oJl8lCRo+Z59VWQlo2M9bswumCoa2jGN6mbin1UkRERKRsxIa5R0od0UgpERGRSq9Ck1Lz5s1jx44dvPnmmzz88MMsXLiQtWvXFtpuz5493H777fTv35/333+fVq1aMXr0aDIzMwH49ddfuffee7n55pv54IMP8PPz45577inv06l4OZkE/vAIAFmdJuEMjil5U3Yn01b/SYo1l+Z1gpjS/0IMBtWREhERkcotNn+kVIpGSomIiFR2FZaUysrKYsWKFcyYMYPWrVszcOBAxo0bx7Jlywpt++6779KhQwcmT55M48aNuf/++wkODmbVqlUALF26lKuuuopRo0bRuHFjZsyYQWJiIklJSeV9WhUqcMtz+GQewxHSkKz2t59XW898vY8d8ekEm008ObQlFl+fUuqliIiISNnJrymVlJVLVo6jgnsjIiIiZ1NhSaldu3Zht9vp0KGDZ1mnTp3Ytm0bTqfTa9vDhw/Ttm1bz2eDwUCzZs3YunUrAJs3b2bgwIGe9fXr12f9+vXUqlWrbE+iEvFJ3of/1lcByOg1G0yWErf16R/HWLktHoA5Q5p7hsGLiIiIVHbBFhOhFvcE03GqKyUiIlKpVVhSKjExkfDwcPz8/DzLIiMjsdlspKSkeG0bGRnJsWPHvJYlJCSQnJxMWloaqampOBwObrnlFnr06MH48eMLbV+tuVwEfTsTgzMXW8P+5DQaUOKm9iZm8viXewC4pVsDejaOKK1eioiIiJSLGNWVEhERqRIqLClltVq9ElKA53NOTo7X8sGDB/P555+zYcMG7HY7H374Ib///ju5ublkZWUB8OijjzJ06FBefvllcnJyuP322wuNuDoXg6HsXmXZvvng5/gd/gaX0Y/MXrNK3E5mjp0pn+zEZnfSrVE4t13SsErGo6q9FAvFQ/FQLBSPyh8PqVpiQt0jxuNSlZQSERGpzEwVdWCz2Vwo+ZT/2WLxfvSsd+/eTJgwgUmTJuFwOOjatSvDhg0jIyMDHx93raNrr72W4cOHA7BgwQJ69OjB1q1b6dixY5H7FBERfB5nVEHt51rh+zkAGHr8h1oXtj3HDqfncrmY/vYWDqdkExPmz8s3diY80O/cO56Hso53VaJYeFM8vCkeBRQLb4qHN8VD8qnYuYiISNVQYUmpqKgokpOTsdvtmEzubiQmJmKxWAgJCSm0/fjx47nllltIT08nIiKCyZMnExMTQ3h4OL6+vjRu3NizbXh4OGFhYSQkJBSrTydPpuNynd95nY7B4L5QLov2AzY9RUDqIRxB0SS3ug1OpJeonTc2HeaLP47h62Pg8Sta4LDaOGG1lW5n85RlPKoaxcKb4uFN8SigWHhTPLyVdTzy25eqI7/YeZwe3xMREanUKiwp1bJlS0wmE1u3bqVz584AbNmyhTZt2mA0ej9VuHr1arZt28aMGTOIiIggOzubTZs28eSTT2IymWjdujW7du1iyJAhACQlJZGcnExMTEyx+uRyUaYX96XdvjH1b/x/fQmAjB4zcZkCoATt/3womZe+OwDAff0upFXd4HK5ySnreFclioU3xcOb4lFAsfCmeHhTPCRfTP5IKRU6FxERqdQqrKaUv78/w4cPZ9asWWzfvp1169axdOlSbrrpJsA9aio72/3tVqNGjVi+fDlffPEFBw8e5N5776VevXr07t0bgJtvvpm3336bzz77jH379jF9+nRatmzpNWNfdRT0/RwMDhs5MT3IaXJFido4lm5jxupdOF1wZesorm5Tt5R7KSIiIlK+8mcOjk+zYXcqUykiIlJZVdhIKYBp06Yxa9YsRo8eTVBQEJMmTWLQoEEA9OzZkyeeeIIRI0Zw0UUXMWvWLJ588klSUlLo3r07ixcv9oyouvzyy0lLS2P+/PmcPHmSLl268NJLL2GoxpVJff/egPnA57iMJjJ6P1KiKqy5DifTVv1BsjWXZrUDmdr/wmodMxEREakZagf54edjIMfhIiEt25OkEhERkcqlQpNS/v7+zJ07l7lz5xZat3v3bq/P11xzDddcc80Z27ruuuu47rrrSr2PlZLDRtC3MwGwthmLo1azEjXz7Nf7+T0+nWCziblXtcLi61OavRQRERGpEEaDgZhQfw4kZRGXoqSUiIhIZVVhj+9Jyflvew1T6gGc/rXJ6nJ3idr47M9j/G/rUQBmD26uizURERGpVlRXSkREpPJTUqqKMWYcJfDn5wDI6DEDl1/xZwPaeyKTx7/YA8DYbg3o1SSiVPsoIiIiUtHyv3DTDHwiIiKVl5JSVUzgD49hsGeRW+9ibM3O/DjjmWTY7Ez95A+y7U66NQzntu4Ny6CXIiIiIhUrJjR/pJSSUiIiIpWVklJViG/cD1j2fIwLAxm9il/c3OVyMXvtbg4lW4kKNvPIkBb4GFXYXERERKqf2PzH91L0+J6IiEhlpaRUVeHIJWjjQwBkX3Qj9toXFbuJt38+wtd7T+LrY2Du0JaEBfiWdi9FREREKoXY0ILH91wuVwX3RkRERE5HSakqwn/Hm5iSduO0hJPZ9f5i759izeWl7w8CcG/fJrSuF1LKPRQRERGpPKJDLRiArFwHydbciu6OiIiInIaSUlWAISuRgM1PAZDZbSouS3ix2zhwMguH00W9EDMj2tYr7S6KiIiIVCp+JiN1gs0AHFGxcxERkUpJSakqIPDHJzHmpJNbuy3ZLf9Vojbi8qZDjg3zx1DMWlQiIiIiVZHqSomIiFRuSkpVcqaELfjveg+AjN6PgNGnRO3kf0OYf3EmIiIiUt2dWldKREREKh8lpSozp8NT3Nza4nrsdTuVuKn8bwjzL85EREREqruY/JFSqRopJSIiUhkpKVWJWf58F9/E7Tj9Qsjs/sB5tRWXqpFSIiIiUrPEhLqvezRSSkREpHJSUqqSMmQnE/jjkwBkdbkHV0Dt82ov//G9mDCNlBIREZGaITbvuudIqpJSIiIilZGSUpVU4Kb5GG0p2Gs1x9pmzHm1lWGzk5I3FXL+N4YiIiIi1V3+CPGTmTlYcx0V3BsRERH5JyWlKiFT4g4sO94G8oubm86rvfwh62H+vgSZz68tERERkaoixOJLiMV97aNH+ERERCofJaUqG5eLoI0PYsBFdtNh5MZcct5NxuUV91Q9KREREalp8keJ50/6IiIiIpWHklKVjPmv9/FN+AWXKYDMS2aUSpueelJ6dE9ERERqGNWVEhERqbyUlKpEDDnpBH3/GACZF0/GGRRdKu0e8YyUUpFzERERqVnyR4prpJSIiEjlo6RUJRKw+RmM1kTsYY2xthtXau3mj5TS43siIiJS08SGur+UU00pERGRykdJqUrC5+Ru/LcvASCj1xzwMZda23F53wzmX5SJiIiI1BQxeV/K5dfYFBERkcpDSanKwOUi6NuZGFwObBdcRm6DS0ut6VyHk4R0G1BwUSYiIiJSU+TX1DyaZsPudFVwb0RERORUSkpVAn771uAX9z0uHzMZPR8u1bbj02w4XWA2GYkM9CvVtkVEREQquzrBZvx8DDicLo6l6xE+ERGRykRJqYqWm0XQ97MByOp4J86QBqXafP5Q9ZhQCwaDoVTbFhEREansjAYD0aH5xc6VlBIREalMlJSqYAFbXsAnIx5HcH2yOt5Z6u0XFDlXPSkRERGpmfKvg+I0A5+IiEiloqRUBTKmHCDgt8UA7sf2TKWfOMqf/lgz74mIiFQfNpuN6dOn07lzZ3r27MnSpUvPuO3XX3/NsGHD6NChA0OHDuWrr77yWt+5c2eaN2/u9crMzCzrUyhXMRopJSIiUimZKroDNVnQd7MwOHPIadCHnAsuK5Nj5E9/HKOZ90RERKqNefPmsWPHDt58802OHj3K1KlTiY6O5vLLL/fabteuXUycOJEpU6bQp08fvvvuOyZPnszKlStp0aIFx44dIz09nXXr1mGxFHyBFRAQUN6nVKbyR0odSVVSSkREpDJRUqqC+B1ch/nvr3AZfcnoOQfKqN7TkfyaUhopJSIiUi1kZWWxYsUKXn31VVq3bk3r1q3Zs2cPy5YtK5SUWr16Nd26deOmm24CoGHDhqxfv57PPvuMFi1asG/fPmrXrk39+vUr4lTKTf6I8SN6fE9ERKRSUVKqItizCfrWPcuetf2tOMKblMlhXC6XZ6RUbKiSUiIiItXBrl27sNvtdOjQwbOsU6dOLFq0CKfTidFYUJ3h6quvJjc3t1Ab6enpAOzdu5cLLrig7DtdwWJD82tKZeNyuTT5i4iISCWhmlIVIGDrYnzS/sYRGEVmp8lldpyTWblk250YDXhmnREREZGqLTExkfDwcPz8/DzLIiMjsdlspKSkeG3bpEkTWrRo4fm8Z88efvzxR7p37w7Avn37sFqt3HjjjfTs2ZNbb72VAwcOlMt5lKd6oRYMQFaugxRr4SSdiIiIVAyNlCpnxrQjBGx5AYDMSx4Cv8AyO1b+DDNRwWZ8fZR/FBERqQ6sVqtXQgrwfM7JyTnjfklJSUyaNImOHTvSv39/APbv309qair33HMPQUFBvPrqq4wZM4Y1a9YQFBRU5D6V1cCj/HbPt32Lr5E6wX4cS88hLjWbWoF+596pkimtWFQXioc3xaOAYuFN8fCmeBQo61gUtV0lpcpZ0A9zMNizyYnuiq3psDI9Vv4MMzFhKnIuIiJSXZjN5kLJp/zPpxYrP9WJEye4+eabcblcPP/8855H/JYsWUJubi6Bge4vyRYsWECfPn3YsGEDQ4cOLXKfIiKCS3Iq5dp+o8ggjqUnkeqAyMiy7W9ZKutYVzWKhzfFo4Bi4U3x8KZ4FKjoWCgpVY58D3+Led+nuAw+ZPR6pMzTs/nFPFVPSkREpPqIiooiOTkZu92OyeS+lEtMTMRisRASElJo+2PHjnkKnb/11lvUqlXLs87Pz89r1JXZbCY2NpZjx44Vq08nT6bjcpXkbM7OYHBfLJdG+1GBvgD8eTiZnvULx6myK81YVAeKhzfFo4Bi4U3x8KZ4FCjrWOS3fy5KSpUXew6BGx8CwNpmNI7IVmV+yPxpj2M1UkpERKTaaNmyJSaTia1bt9K5c2cAtmzZQps2bbyKnIN7pr5x48ZhNBp56623qF27tmedy+Vi4MCB3HnnnYwYMcKz/d9//03jxo2L1SeXizK9uC+N9vOvh46kWKv0jUhZx7qqUTy8KR4FFAtvioc3xaNARcdCSanysnkxpuS9OP0jyOpyb7kcMr+mVIxGSomIiFQb/v7+DB8+nFmzZvH4449z/Phxli5dyhNPPAG4R00FBwdjsVhYvHgxhw4d4u233/asA/djfsHBwVx66aW88MILxMTEUKtWLZ577jnq1q1Lnz59Kuz8ykr+9VB+eQMRERGpeEpKlQND5jH4+kkAMrpPx2UOLZfj5l90xYYpKSUiIlKdTJs2jVmzZjF69GiCgoKYNGkSgwYNAqBnz5488cQTjBgxgs8//5zs7GyuvfZar/2vvvpqnnzySe6//35MJhP33nsvGRkZdOvWjVdeeQUfH5+KOK0y5RkplaqklIiISGWhpFQ5CPzhCcjJIDeqA7YW1557h1KQmWMnOW/KYz2+JyIiUr34+/szd+5c5s6dW2jd7t27Pe/Xrl171nbMZjMPPPAADzzwQKn3sbLJ/5LuZGYO1lwH/r7VL/EmIiJS1RjPvYmcL9PxbWDwIbP3o2Aon5DH5Y2SCrWYCDIr9ygiIiI1W4jFl+C8a6I4jZYSERGpFJStKAdpV75BrUAXdlMjKKcCYipyLiIiIuItNszCn8cyiEuxcmFkYEV3R0REpMbTSKly4AxtCHXblOsx84ucq56UiIiIiFtMaP4MfBopJSIiUhkoKVVN5V9sxWiklIiIiAhQ8GXdkbwv70RERKRiKSlVTeVfbOVPfywiIiJS03mSUqopJSIiUikoKVVNFdSUUlJKREREBApqbcZppJSIiEiloKRUNWR3ODmWlpeUCtXjeyIiIiJQMIL8aJoNu7OcZp8RERGRMyp2Umrq1Kls3LgRh8NRFv2RUpCQbsPhArPJSGSQX0V3R0RERKRSqBNsxtfHgMPp4li6HuETERGpaKbi7hAUFMSMGTPIzc1l0KBBDBkyhK5du2IwGMqif1IC+fWkokMtGPXfRURERAQAo8FAdIiFv5OtHEnJ9szGJyIiIhWj2COlHnroITZu3Mjzzz+PyWTivvvuo1evXjz22GNs3bq1DLooxZU/816sipyLiIiIePHUlVKxcxERkQpXoppSBoOBLl26MHPmTNauXcvIkSP53//+x7/+9S/69+/P4sWLsdlspd1XKSJPUipM3/6JiIiInCp/EhgVOxcREal4xX58DyAzM5MNGzawdu1avvvuO6Kiorj55psZMmQIiYmJLFiwgM2bN7NkyZLS7q8UQVyq+yIrRiOlRERERLzE5H1pl/8lnoiIiFScYielxo8fzw8//EBISAiDBw/mrbfeom3btp71zZo1Iy0tjRkzZpRqR6Xo8oeja6SUiIiIiLf88gZHNFJKRESkwhU7KRUZGcnixYvPWty8c+fOrFix4rw7J8Xncrk8F1kxYRopJSIiInKqU2tKuVwuTdYjIiJSgYpdU+qRRx5h3759rFmzxrNswoQJvPvuu57PtWvXpkmTJqXTQymWpKxcrLlODEB0iJJSIiIiIqeKDrVgADJzHKRYcyu6OyIiIjVasZNSzzzzDIsWLSIgIMCzrGvXrrz00ku8+OKLpdo5Kb78UVJRwWb8TCWqYy8iIiJSbZlNRmoH+QGqKyUiIlLRip21eP/993nmmWfo16+fZ9lNN93EggULeO+990q1c1J8BfWkNEpKRERE5HTyH+E7kqq6UiIiIhWp2Ekpq9VKUFBQoeXh4eGkp6eXSqek5ArqSanIuYiIiMjp5M9QHKeRUiIiIhWq2EmpXr168dhjj3H06FHPsmPHjjF37lx69uxZqp2T4ssfhp5/sSUiIiIi3gpGSikpJSIiUpGKnZSaOXMmubm59O/fn27dutGtWzcuvfRSnE4nM2fOLIs+SjEUPL6nkVIiIiIip5Nf5iAuRY/viYiIVCRTcXeoVasWy5cvZ9euXRw8eBCTyUSjRo248MILi31wm83G7Nmz+eKLL7BYLIwdO5axY8eedtvvvvuOefPmcfjwYdq1a8fMmTNp3Lhxoe0+++wz7rrrLnbv3l3s/lQH+Y/vqaaUiIiIyOnllzlQoXMREZGKVaLp2ex2O+Hh4bRt25ZWrVrh7+/PgQMH+PTTT4vVzrx589ixYwdvvvkmDz/8MAsXLmTt2rWFttuzZw+33347/fv35/3336dVq1aMHj2azMxMr+3S0tJ47LHHSnJK1UJWjoOkLPfUxrGhGiklIiIicjqxeWUOTmTmkJ3rqODeiIiI1FzFHim1bt06HnroIVJSUgqtq127NkOGDClSO1lZWaxYsYJXX32V1q1b07p1a/bs2cOyZcu4/PLLvbZ999136dChA5MnTwbg/vvv5+uvv2bVqlWMGjXKs928efOoX78+iYmJxT2taiEubwaZUIuJYEux/9OKiIhIGdu3bx916tQhODiYb7/9lvXr19OqVSuuvfbaiu5ajRLq70uw2US6zc6R1GwujAys6C6JiIjUSMUeKfXUU08xcOBA1qxZQ0hICMuXL2fRokXExMRw1113FbmdXbt2Ybfb6dChg2dZp06d2LZtG06n02vbw4cP07ZtW89ng8FAs2bN2Lp1q2fZ5s2b2bx5M3fccUdxT6na8BQ5Vz0pERGRSue9997jqquu4s8//+SPP/5g/PjxHD58mOeee47nnnuuortX46iulIiISMUr9nCaw4cPs3jxYho0aMBFF11EYmIiAwYMwGg0Mm/ePEaMGFGkdhITEwkPD8fPz8+zLDIyEpvNRkpKCrVq1fJafuzYMa/9ExISCA0NBSAnJ4eHHnqImTNn4uvrW9xT8jAYSrxrkdotq/bz5Y+Uig2zlPmxzkd5xaMqUCy8KR7eFI8CioU3xcNbWcejtNp97bXXmDt3Ll26dOGRRx6hZcuWvPbaa/z888/cfffdnhHhUj5iQv3581iG6kqJiIhUoGInpUJCQrBa3cmPCy64gF27djFgwAAaN27MkSNHityO1Wr1SkgBns85OTleywcPHsydd97JlVdeSa9evVi1ahW///47Xbt2BeDFF1+kdevW9OzZk02bNhX3lDwiIoJLvG9laP9EtrsmQtN6IURGlu2xSkNZx6MqUSy8KR7eFI8CioU3xcNbZY/HsWPH6NSpEwAbNmzg+uuvB6Bu3bqF6mRK2YvJGyl1RCOlREREKkyxk1J9+vRh9uzZzJkzh65duzJv3jz69u3L559/Tp06dYrcjtlsLpR8yv9ssXjPHNe7d28mTJjApEmTcDgcdO3alWHDhpGRkcFff/3F//73P1atWlXcUynk5Ml0XK7zbqYQg8F9oVxW7efbdywdgFp+Ppw4kV52BzpP5RWPqkCx8KZ4eFM8CigW3hQPb2Udj/z2z1fjxo1ZtWoVtWrV4ujRowwYMIDc3FyWLl1KixYtSqGnUhz5xc7jUjVSSkREpKIUOyk1Y8YMHnvsMXbs2MGwYcP4/PPPGTlyJAEBAcyfP7/I7URFRZGcnIzdbsdkcncjMTERi8VCSEhIoe3Hjx/PLbfcQnp6OhEREUyePJmYmBi++OILUlNTGThwIAAOh3u0UIcOHZg9ezZXXXVVkfvkclGmF/dl3X7+N30xoZYqcZNS1vGoShQLb4qHN8WjgGLhTfHwVtnjMXXqVO666y5SU1P597//TZMmTZgzZw5ffvklixYtquju1TixeTU4lZQSERGpOMVOSn399ddMmTKF8PBwABYsWMCsWbMwm83FqufUsmVLTCYTW7dupXPnzgBs2bKFNm3aYDR6119fvXo127ZtY8aMGURERJCdnc2mTZt48sknad++PUOHDvVsu23bNu6//34++ugjIiIiint6VZbd6SI+zQYUXGSJiIhI5dG9e3d+/PFH0tPTPXUx77zzTqZNm3ZeNTGlZPILnR9NzcbhdOFjVJE2ERGR8lbs2fdmz55NcnKy17KgoKBiX0z5+/szfPhwZs2axfbt21m3bh1Lly7lpptuAtyjprKz3d9cNWrUiOXLl/PFF19w8OBB7r33XurVq0fv3r0JCwujYcOGnldUVBQADRs2JCgoqLinV2UlpLkvqPx8DNQO8jv3DiIiIlLuvvvuO+x2OwArV65k+vTpvPjii4VKGkjZqx1kxtfHgN3p4li6raK7IyIiUiMVOynVtWtXVq9eXSoXT9OmTaN169aMHj2a2bNnM2nSJAYNGgRAz549+fTTTwG46KKLmDVrFk8++aRndr/FixcXGlFVk8XlzRwTE+qPUdMxiYiIVDovvvgikydP5siRI2zevJmZM2dSr149vvzyS5544omK7l6N42M0EB2iYuciIiIVqdiP7508eZKXXnqJRYsWUatWLcxms9f6r776qsht+fv7M3fuXObOnVto3e7du70+X3PNNVxzzTXnbLNr166F9q0JjqTm1ZMKs5xjSxEREakI//vf/3jhhRdo164dM2bM4OKLL2b27Nn8/vvvjBs3jocffriiu1jjxIb583eylSOp2XSp6M6IiIjUQMVOSl133XVcd911ZdEXOQ8FI6WUlBIREamMUlNTady4MS6Xi6+//ppbb70VcJdByJ+oRcpXfl2pOI2UEhERqRDFTkpdffXVZdEPOU9H8maOUZFzERGRyqlFixYsWbKEsLAwkpKSGDhwIMeOHePpp5+mffv2Fd29Gik6NP/xPc3AJyIiUhGKnZS68cYbMZylZtFbb711Xh2SksmvhRCrx/dEREQqpVmzZjF16lTi4uK45557iImJ4bHHHiMuLo7nnnuuortXI+V/mReXqqSUiIhIRSh2Uqpr165en+12O4cPH+abb75h/PjxpdYxKTqXy+V5fC82VCOlREREKqMWLVrw8ccfey27//778fPTrLkVJf/LvCMpVlwu11m/eBUREZHSV+yk1MSJE0+7/IMPPuCLL77glltuOe9OSfEkW3PJynVgoGAYuoiIiFQ+f/zxB0uWLGH//v04HA4uuOACbrjhBrp0UZntipA/+15mjoNUq52wAN8K7pGIiEjNYiythi6++GJ+/PHH0mpOiiG/DkKdYDN+plL7TyoiIiKl6Msvv+S6667D5XIxYsQIRowYgcFgYOzYsaxbt65YbdlsNqZPn07nzp3p2bMnS5cuPeO2X3/9NcOGDaNDhw4MHTq00EzJq1evZsCAAbRr144JEyaQlJRUovOriiy+PtQJco9Uy5/JWERERMpPsUdKHT16tNCyzMxMlixZQkxMTKl0Soonv56UZt4TERGpvJ577jnuu+8+xowZ47X8jTfe4IUXXmDAgAFFbmvevHns2LGDN998k6NHjzJ16lSio6O5/PLLvbbbtWsXEydOZMqUKfTp04fvvvuOyZMns3LlSlq0aMH27duZMWMGs2fPpkWLFjz22GNMmzaNxYsXl8YpVwkxYf4cz8jhSEo2F9ULqejuiIiI1CjFTkr169cPg8Hg9dy9y+WiXr16PP7446XeQTm3OM/Me0pKiYiIVFaHDx+mb9++hZb37duXp59+usjtZGVlsWLFCl599VVat25N69at2bNnD8uWLSuUlFq9ejXdunXjpptuAqBhw4asX7+ezz77jBYtWvDOO+8wePBghg8fDriTXX379uXw4cPUr1+/5CdbhcSGWvjtSKrnSz4REREpP8VOSv1zyLfBYMDX15fIyEgVh6wgcZ6Z91TkXEREpLJq0qQJGzdu5MYbb/Ra/s033xRrtPmuXbuw2+106NDBs6xTp04sWrQIp9OJ0VjwKP/VV19Nbm5uoTbS09MB2LZtG7feeqtneb169YiOjmbbtm01JymVd/10RDPwiYiIlLtiJ6ViYmJYtmwZoaGhXHnllYC7+HmPHj3417/+VeodlHPLrymlx/dEREQqr0mTJjFp0iS2bdtGu3btANi6dSuff/458+bNK3I7iYmJhIeHe83aFxkZic1mIyUlhVq1anmWN2nSxGvfPXv28OOPPzJq1CgAjh8/Tp06dby2iYiIICEhodjnV1XljzSP00gpERGRclfspNQzzzzD+++/z5w5czzLunTpwksvvURSUhITJkwo1Q7KuR3xPL6nkVIiIiKVVd++fXn11Vf573//y7vvvovZbOaCCy7gv//9L23bti1yO1ar1SshBXg+5+TknHG/pKQkJk2aRMeOHenfvz8A2dnZp23rbO2cTlkNls9vtywH48fkJaWOpGSX6XHOV3nEoipRPLwpHgUUC2+KhzfFo0BZx6Ko7RY7KfX+++/z7LPP0rlzZ8+ym266iebNm3P//fcrKVXOrLkOTma6LxxVU0pERKRy6969O927d/daZrPZilXDyWw2F0oa5X+2WE5/LXDixAluvvlmXC4Xzz//vOcRvzO15e9fvC+6IiKCi7V9cZVl++38zQCcyMwhMCQAfz+fMjtWaSjrWFc1ioc3xaOAYuFN8fCmeBSo6FgUOylltVoJCgoqtDw8PNxTn0DKT1zeo3shFhMhFt8K7o2IiIgU1+bNm7ntttv4888/i7R9VFQUycnJ2O12TCb3pVxiYiIWi4WQkMKzxx07dsxT6Pytt97yerwvKiqKEydOeG1/4sQJateuXaxzOHkyHZerWLsUicHgvlguq/bBPWFPkNmHDJuD7fsTaRIZWDYHOk/lEYuqRPHwpngUUCy8KR7eFI8CZR2L/PbPpdhJqV69evHYY48xd+5coqOjAffFzty5c+nZs2fxeyrnJX+mGNWTEhERqRlatmyJyWRi69atnpHrW7ZsoU2bNl5FzsE9U9+4ceMwGo289dZbhZJN7dq1Y8uWLYwYMQKA+Ph44uPjPTWvisrlokwv7su2fQOxof7sOp7B4eRsGkdUzqRUvrKOdVWjeHhTPAooFt4UD2+KR4GKjoXx3Jt4mzlzJrm5ufTr149u3brRrVs3+vTpg8Ph4OGHHy6LPspZ/H97dx7eVJn+f/x9krRJutMVaJF9LYgsggoI4u64ILOhDuLPdRxF56szKuAo6ijiNs64jMsMI64o7qjjguM648YuIMgmW1tooXuapEnO7480KbFFqTRJSz+v6+rV9uTkyXNuCjy9cz/32VkZanKuflIiIiIdgdPpZNKkScyePZtVq1axePFi5s2bF66GKi0txe0Org8effRRtm3bxty5c8OPlZaWhqvbzznnHF577TUWLlzIunXruO6665gwYUKHufNeSLjZeaWanYuIiMRSiyulMjMzWbBgAevXr2fLli3YbDZ69OhBnz59ojE/+RGhSin1kxIREek4ZsyYwezZs5k2bRopKSlMnz6dk046CYCxY8cyZ84cJk+ezDvvvIPb7eaXv/xlxPPPPvts7rzzToYNG8att97K3/72NyorKxkzZgy33XZbPC4prvIbbhYTuqOxiIiIxEaLk1Jer5f777+f/Px8zjvvPAAmT57MMcccw9VXX01CgvoaxVLjnfeUlBIREWlrvvrqqx89Z/369S0e1+l0Mnfu3HAF1P7Ge/vtt390rMmTJ4e373VUBemhO/CpUkpERCSWWpyU+vOf/8zSpUu59dZbw8d+97vfcf/99+N2u7nxxhtbdYLyw3aGK6W0fU9ERKStmTp16gGdZ+je1HEVWkeF2iKIiIhIbLQ4KfXuu+/yr3/9i4EDB4aPnXDCCeTl5XHZZZcpKRVDvoBJUZUHUKNzERGRtmjdunXxnoIcgPyGivOiSjf+gInVoiShiIhILLS40blpmng8nmaP19fXt8qk5MDsqg4unBKsBrmp9nhPR0RERKRdyk2xY7MY+AImu2uarnNFREQkOlqclDr55JP505/+xJIlS3C5XLhcLpYtW8bs2bM54YQTojFH2Y+dDc04u6Y5sKjsX0REROQnsVoMuqqvlIiISMy1ePvejBkzmDVrFtOmTSMQCGCaJjabjUmTJnHFFVdEY46yH41NztVPSkRERORgFGQ42FZex44KN0ceFu/ZiIiIdAwtTko5nU7uu+8+qqqq2Lp1K36/n++++45FixZxwgknsGbNmmjMU5rR2ORc/aREREREDkZBuhMoZ0eFmp2LiIjESouTUiEbNmzg1Vdf5e2336ampobevXszc+bM1pyb/IjQoilflVIiIiIiByXU7HxnpbbviYiIxEqLklI7d+7k1Vdf5bXXXmP79u2kpaVRU1PDvffey2mnnRatOcp+hHoeFOjOeyIiIiIHJdQOQZVSIiIisXNASamXXnqJV199lSVLlpCbm8vEiRM56aSTOPLIIxk6dCj9+vWL9jzle0zTZKd6SomIiIi0ilA7hB0VdZimiaGbyIiIiETdASWlZs2aRffu3Zk7dy5nnnlmtOckB6Cirp5arx8gfLcYEREREflpuqYF11O1Xj+VdT4ykhLiPCMREZFDn+VATrrjjjsoKChgxowZHH300cyYMYP3338fj8cT7fnJfoSqpHJTErHbDuiPUURERET2w5FgJSclEYAd6islIiISEwdUKTV58mQmT57M3r17+fe//81bb73FlVdeicPhIBAI8MUXX9C9e3cSEvSOUqyoybmIiIhI6ypId1Ba42VnhZvBXdLiPR0REZFDXotKbDIzMznvvPN45pln+OCDD7jiiisYOHAgt912G+PGjWPOnDnRmqd8j5qci4iIyKHMWraWTk+Ng69fjNlrht7sU6WUiIhIbPzkfV+dO3fm4osv5uWXX+btt9/mN7/5DZ988klrzk1+wA41ORcREZFDmLVqO9bKLfDv68BbG5PXbGx2rjvwiYiIxEKrNCPq0aMHV155JW+99VZrDCcHYGeoUipDlVIiIiJy6PH2OB5/eg9w7cG56l8xec2C9OCbfaF1loiIiESXOmS3U+opJSIiIoc0iw3XqP8DwLn87xieqqi/ZLhSqlKVUiIiIrGgpFQ75K73U1brBSBfPaVERETkEOXpOwmy+2PxVOJc9c+ov17ozb7SGi/uen/UX09ERKSjU1KqHdrZ8O5dit1KuuOAbqAoIiIi0v5YrDDhBgCcKx7HcFdE9eXSHTZS7Fagcb0lIiIi0aOkVDsU2rpXkO7EMIw4z0ZEREQkigZNwpc1AIu3CueKx6L6UoZhkN/QV0rNzkVERKJPSal2aGelmpyLiIhIB2Gx4Bp1LQDOVf/EqNsb1ZcLra9C6y0RERGJHiWl2iE1ORcREZGOxNvrFOpzhmCpryVp+d+j+lr54TvwqVJKREQk2pSUaod2NNymuEBNzkVERKQjMAxco/4AgPPrJzBcpVF7qcY78KlSSkREJNqUlGqHQo03C1QpJSIiIh2Et/tE6vOGYfjqSFr2UNReJ5yUUqWUiIhI1Ckp1c74AyZFlaHte6qUEhERkQ7CMKgNVUutfgpLTXFUXib0pl9RpRt/wIzKa4iIiEiQklLtzO4aD76Aic1ikJtij/d0RERERGKmvtux1HcZheH3kLTswai8Rm6KHZvFwBcw2V3jicpriIiISJCSUu1MqJ9U13QHVosR59mIiIiIxJBhUDs6WC3lWPMcluqdrf4SVotB1/TQFj71lRIREYkmJaXamVB/gwJt3RMREZEOqD7/GLz5YzACXpKW/DUqr5Gfrr5SIiIisaCkVDsTTkqlq8m5iIiIdEzhaql1L2Cp3Nrq44f6SikpJSIiEl1KSrUzOxtuT6wm5yIiItJR+bocifew8RgBH8lRqJYKVaQXVWr7noiISDQpKdXONG7fU6WUiIiIdFyhO/HZ17+ItWJzq46dn65KKRERkVhQUqodMU0z3HAz1OtAREREpCPy5Q3D0+NEDDNA0pf3terYoUqpHZV1mKbZqmOLiIhIIyWl2pFKt49arx9QUkpERETENepaAOwbXsO699tWGze0zqrx+Kl0+1ptXBEREYmkpFQ7srOhSionJRFHgjXOsxERERGJL1/OYDy9TsXAbNVqKUeClZyURKBx/SUiIiKtT0mpdqTxznuqkhIREREBqB11LSYGjk1vYC1b22rjhtZb6islIiISPUpKtSM7wnfeU5NzEREREQB/1gA8fc4AIPnLe1tt3K4N660dugOfiIhI1Cgp1Y403nlPlVIiIiIiIa5R12AaFuxb3sG2e1WrjKlKKRERkehTUqodCfU0KEhXpZSIiIhIiL9THzz9zgYg6ct7WmXMgoZKqZ2VSkqJiIhES1yTUh6Ph5kzZzJy5EjGjh3LvHnz9nvup59+yplnnsmwYcO44IIL2Lx5c/gx0zR57LHHmDhxIsOHD2fatGls3LgxFpcQU6FFUb4qpUREREQi1I78PaZhxb71P9hKlh70eKHKdDU6FxERiZ64JqXuuusuVq9ezfz587n55pt58MEHefvtt5uct2HDBi677DKOP/54XnrpJQYNGsS0adOora0FYMGCBcybN48//elPvPTSSxQUFHDJJZdQV3foLCLc9X5213gBVUqJiIh0dC15Yy9kyZIlHH/88U2Ojxw5kv79+0d8hNZY7UkgoyfuAb8AIPmLg6+WCq23dtd4cdf7D3o8ERERacoWrxd2uVwsXLiQxx9/nMLCQgoLC9mwYQPPPPMMp5xySsS5zz33HMOGDePqq68G4I9//CMffvghixYtYsqUKbzyyitceOGFHHfccQDMnj2bUaNGsWzZMsaMGRPza4uGoqpglVRyopV0Z9z+2ERERKQN2PeNvaKiIq6//nq6du3aZA0Vsn79eq6++mrsdnvE8V27dlFdXc3ixYtxOBorsZOSkqI6/2hxjfw9jvUvk7jjExKKPqe+61E/eax0p43kRCu1Xj9FVW56ZSW34kxFREQE4lgptW7dOnw+H8OGDQsfGzFiBCtXriQQCEScu337dg4//PDw94Zh0K9fP1asWAHAddddx5lnnhnxuGmaVFdXR/ciYqixybkTwzDiPBsRERGJl9Abe7NmzaKwsJATTzyRiy++mGeeeabZ8xcsWMCUKVPIyspq8timTZvIycmhW7du5OTkhD/a61ojkNYN98ApACR9cQ+Y5k8eyzCMcF8pNTsXERGJjriV3JSWltKpUycSExPDx7Kzs/F4PFRUVJCZmRlxfNeuXRHPLykpIT09HQiWne9r4cKF+Hw+RowY0aI5RWv9FRr3YMbf2XA74oIMR9TmGSutEY9DhWIRSfGIpHg0UiwiKR6Roh2Pthbn/b2x98gjjxAIBLBYIt9z/Pjjj5k7dy41NTU8+OCDEY9t3LiRnj17xmTeseIaOR3HN8+TWPQ5CTv+S323sT95rIIMB+t317BDfaVERESiIm5Jqbq6uoiEFBD+3uv1Rhw/9dRT+d3vfsfpp5/OuHHjWLRoEV9//TWjR49uMu7KlSuZO3cuF110ETk5OS2aU1ZWaguvomUOZvw97mAvg75d0snOju48YyXa8W5PFItIikckxaORYhFJ8YjUUeLRkjf2AB5++GEAXn755SZjbdq0ibq6OqZOncqWLVsYOHAgM2fObHGiqi29sWemdsU9+Dycq/5F8pd3U9ltzE+e4L534It3clLJ6EiKRyTFo5FiEUnxiKR4NGorb+rFLSllt9ubJJ9C3+/b0wDg2GOP5YorrmD69On4/X5Gjx7NWWedRU1NTcR5y5cv55JLLuHYY48N959qiT17qg+mynu/DCO4UD6Y8TeUVAGQZbdQVta+tyW2RjwOFYpFJMUjkuLRSLGIpHhEinY8QuO3FS15Y+/HbN68mcrKSq655hpSUlJ4/PHHueCCC3jzzTdJSUk54HHa3Bt7J94Aa58joWQp2RVfQt8TftLr9s/PALaz21XfZt4UbEs/i22B4hFJ8WikWERSPCIpHo3iHYu4JaXy8vIoLy/H5/NhswWnUVpaisPhIC0trcn5l19+ORdddBHV1dVkZWVx9dVXk5+fH378iy++4Le//S1jxozh3nvvbVK6fiBM86BaD0R1/J0NvQy6pjkOmV9Aoh3v9kSxiKR4RFI8GikWkRSPSB0lHi15Y+/H/POf/6S+vp7k5GAT73vuuYfx48fzwQcfcMYZZxzwOG3vjb1kkgZPI2nFo9S/dyuVGaN+0lvBGQ0r5c27a+L+pqCS0ZEUj0iKRyPFIpLiEUnxaNRW3tSLW1Jq4MCB2Gw2VqxYEe4JtXTpUoYMGdIkofTGG2+wcuVKZs2aRVZWFm63my+++II777wTgG+//ZbLL7+ccePGcd9994WTXIcKf8AM330vVEYuIiIiHVNL39j7IYmJiRFVV3a7nYKCgia9PH9MW3xjzzX8dzhXP0XC7pUkbHkPb8+TWvy6+enBdVdxlRuf38Rqif9+j46SfD1QikckxaORYhFJ8YikeDSKdyzidvc9p9PJpEmTmD17NqtWrWLx4sXMmzeP888/HwgurtzuYCKmR48eLFiwgHfffZfvvvuOa6+9li5dunDssccCcNNNN9GlSxdmzJhBeXk5paWlEc9v70prPNT7TWwWg7xU+48/QURERA5Z+76xF7K/N/Z+iGmanHDCCRG9plwuF1u3bqVXr16tOeW4MJ1Z1B3+/wBI/uIeMAM/8oym8lLt2CwG9X6T0hpPa09RRESkw4tbUgpgxowZFBYWMm3aNG655RamT5/OSScF38UaO3Ysb731FgCDBw9m9uzZ3HnnnUyePBmARx99FIvFQmlpKcuXL2fjxo1MmDCBsWPHhj9Cz2/vQrch7pruaBPv0ImIiEj8tOSNvR9iGAYTJkzggQce4IsvvmDDhg1cd911dO7cmfHjx0f7MmLCNey3BBJSsO1ZS+Lmf7f4+VaLQdf04JbI0HpMREREWk9c97k5nU7mzp3L3Llzmzy2fv36iO9//vOf8/Of/7zJeTk5OU3OPdSEbkOcn96yPhEiIiJyaJoxYwazZ89m2rRppKSkNHljb86cOeE38n7IH//4R2w2G9deey01NTUcddRRPPbYY1it1mhfQkyYjk7UDb2Y5CX3k/zlfXh7ngKWll1bfrqDbeV1bNnrYuRhGdGZqIiISAd1aDVfOkTtqFQ/KREREWnUkjf2QiZPntwkUWW327nhhhu44YYbojLPtqDuiEtwfv0vbHvXY9+4CE+/SS16/pAuaXz2XTmP/28r43tnkatWCiIiIq0mrtv35MCE7rynSikRERGRljHt6dQdcSkASV/dBwFfi54/9cgC+uUkU15Xzw2LvqHe3/LeVCIiItI8JaXagZ2Vwe17BRlKSomIiIi0VN3hFxFwdMJWsRn7t6+26LmOBCtzzxxEit3K18VV/O3jLdGZpIiISAekpFQ7EGqsma/teyIiIiItZiam4Bp2OQDJX/0F/PUten5BhpPZpwwAYMGynby7bnerz1FERKQjUlKqjausq6faEywzL9D2PREREZGfpG7IBQSc2VirtuJYv7DFzx/fJ4tpo7oBcPu7G9iyx9XaUxQREelwlJRq40JNzrOTE3EkHBp3whERERGJuYQkXMOvACDpq7+C39viIX47pgcju6Xjqvdz/etrcXn9rT1LERGRDkVJqTZuZ4X6SYmIiIi0hrrBv8GfnIe1ZieObxa0+Pk2i8GffzaQnJREtux1cfu732KaZhRmKiIi0jEoKdXGqZ+UiIiISCuxOXGNmA5A0pK/gc/d4iGykhOZc/pArBaDd9eXsnBFUWvPUkREpMNQUqqNC915L1/9pEREREQOmnvQOfhTumKtLcG55umfNMbQ/HSuOrYnAH/5cDOriqpac4oiIiIdhpJSbVyoUkrb90RERERagdWOa+RVACQtfQjq637SMOcMz+eEftn4AiYzFq2l3NXyHlUiIiIdnZJSbdyOUE+pdG3fExEREWkN7gG/xp92GJa6Upyr5/+kMQzD4MaT+9G9k5PdNV5ufHMd/oD6S4mIiLSEklJtmMcXoLQm+K6bKqVEREREWok1gdqRvwcgadnDGN6anzRMcqKNuWcOwmGz8OW2Ch77bGsrTlJEROTQp6RUG1ZU6cYEkhOtZDgT4j0dERERkUOGp/9kfOk9sbj34lz1r588Tu/sZGad1A+AeZ9v49PNe1priiIiIoc8JaXasNDWvfx0B4ZhxHk2IiIiIocQiw3Xkf8HgHPFIxien96s/JSBufzyiK4A3Pzv9eEb1YiIiMgPU1KqDdtRGWpyrn5SIiIiIq3N0/csfJ36YfFU4lz5j4Ma6/fjezG4SypVbh83vP4NHl+glWYpIiJy6FJSqg3buU+llIiIiIi0MouV2lHXAOBc+TiGu/wnD5VoszDn9IGkO2ys213DPf/Z2FqzFBEROWQpKdWG7QxXSikpJSIiIhIN3t6n4csaiMVbjXPF4wc1Vuc0B7f/bCAG8OrXJSxaXdI6kxQRETlEKSnVhoV7Smn7noiIiEh0GBZqR10LgHPVPzHq9h7UcKN7dOLSY7oDMPf9jXy7+6fd2U9ERKQjUFKqjQqYJkWqlBIRERGJOm/Pk6nPORxLfS1Jyx8+6PEuPOowjunZCY8vwPWL1lLt9rXCLEVERA49Skq1UburPXj9JlaLQV6qklIiIiIiUWMYuELVUl8/gVG7+6CGsxgGt546gC5pdnZUuLnl7fWYptkaMxURETmkKCnVRoX6SXVNs2OzGHGejYiIiMihzdt9IvV5wzB87laplkp3JnDnGYNIsBp8tGkPT321oxVmKSIicmhRUqqN2lkRTEqpn5SIiIhIDBgGtaP/CIBz9VNYaooPeshBnVP5w8Q+ADz06RaWbq846DFFREQOJUpKtVE7KhuanKdr656IiIhILNQXjMPbZTSG30PS0gdbZcyzh3TmZ4NyCZgw841vKK3xtMq4IiIihwIlpdqoHRWhJueqlBIRERGJCcPANfoPADjWPoul6uC33BmGwQ0n9KVPdjJ7XfXMfOMbfP7AQY8rIiJyKFBSqo3aURGslCpQpZSIiIhIzNTnH403fwxGoJ6kpX9rlTEdCVbmnjmI5EQrK3ZW8cAnW1plXBERkfZOSak2KtToXJVSIiIiIrEV6i3lWPcClsqtrTLmYZ2c3HxKfwCeXbqT978tbZVxRURE2jMlpdqgKnc9VW4fAPkZqpQSERERiSVfl5F4D5uAEfCRvOT+Vhv3uL7ZTB1ZAMBt73zLd3tdrTa2iIhIe6SkVBsU6ieVlZyIM8Ea59mIiIiIdDy1o4K9pezrX8JavqnVxv3duJ4MK0in1uvn+tfXUlfvb7WxRURE2hslpdqg8NY99ZMSERERiQtf3hF4epyIYQZI+uovrTauzWJwx+kDyUpOZPMeF3Pe24Bpmq02voiISHuipFQbFGpyrq17IiIiIvETrpba8BoJO//XauNmJydyx+kDsBrw729289LK4lYbW0REpD1RUqoN2lkRqpRSk3MRERGRePHnFOLufToGJhmv/oqUD/6IUbe3VcYeXpDBlcf2AuDeDzaxpriqVcYVERFpT5SUaoN2VKpSSkRERKQtqDluLnUDfg2Ac+1zZD4zDsfqpyFw8L2gzhuRz3F9s/EFTG5Y9A0VrvqDHlNERKQ9UVKqDQo1Oi/IUKWUiIiISDyZ9nRqjr+X8smv4ssahMVTSepHN5Dx0pnYdq04qLENw+Cmk/txWCcnJdUe/vTWOvwB9ZcSEZGOQ0mpNsbrC7C72gNAgSqlRERERNoEX5eRlP/qLarH3UogMZWE3SvJePEMUj68AcNd/pPHTbHbmHvGIOw2C59vLeefn29txVmLiIi0bUpKtTFFlW5MICnBSidnQrynIyIiIiIhFhvuwy9k77kf4e7/cwxMnGueJvOZY3GsfQ7MwE8atk9OMjNP7AvAPz7bxv+2tE7fKhERkbZOSak2ZmdlcOtefoYDwzDiPBsRERER+T4zOZfqE/5Kxdkv4svsj8VdTuoHfyTjpbOwlX79k8Y8bVAePx/aBRO46a11FFe5W3fSIiIibZCSUm3MjoqGJufp2ronIiIi0pbVdz2K8l+9Tc3Y2QQSUkjYtZyMF04j5aNZGO6KFo93zYTeDMxLodLt4/rX1+L1/bTKKxERkfZCSak2ZkelmpyLiIiItBvWBOqGXkz5eR/i7jspuKVv9Xwynx2P/ZsXWrSlL9FmYe6Zg0h32PhmVw33fbgpihMXERGJPyWl2phQpZSanIuIiMj+eDweZs6cyciRIxk7dizz5s370ecsWbKE448/vsnxN954gxNOOIGhQ4dyxRVXsHev+hn9FIHkzlSf9CAVk17A16kflro9pP3nGjJenoy1dM0Bj9MlzcGtpw3AAF5aWcxba3dFb9IiIiJxpqRUG7OzoqFSKl2VUiIiItK8u+66i9WrVzN//nxuvvlmHnzwQd5+++39nr9+/XquvvpqTNOMOL5q1SpmzZrFlVdeyfPPP09VVRUzZsyI9vQPafX5x1D+63eoOeZGTFsSCSVL6LTwVJI/uQnDU3VAYxzTM5OLjz4MgDve28DG0tpoTllERCRulJRqQwKmyc7Khp5SqpQSERGRZrhcLhYuXMisWbMoLCzkxBNP5OKLL+aZZ55p9vwFCxYwZcoUsrKymjz29NNPc+qppzJp0iQGDBjAXXfdxUcffcT27dujfRmHNmsCdcN+y97zPsTd50wMM0DSqnlkPjMe+/oX4XvJweZcdFR3jurRCY8vwPWL1lLj8cVg4iIiIrGlpFQbUlbjxes3sRrQOU1JKREREWlq3bp1+Hw+hg0bFj42YsQIVq5cSSDQtH/Rxx9/zNy5c7nggguaPLZy5UpGjhwZ/r5Lly507dqVlStXRmXuHU0gpSvVJz9MxZnP4cvojaWulLTFvyf9lV9g3fPNDz7XajG47dQB5KXa2VZexy1vf9uk0k1ERKS9U1KqDdnRUCXVOc2BzWLEeTYiIiLSFpWWltKpUycSExPDx7Kzs/F4PFRUVDQ5/+GHH+akk05qdqzdu3eTm5sbcSwrK4uSkpJWnXNHV99tHOVT3qPm6BmYNieJxV/Q6flTSP70Fgxv9X6fl5GUwNwzBmKzGHywoYx/fLIlhrMWERGJPlu8JyCNdoT6SWnrnoiIiOxHXV1dREIKCH/v9XpbNJbb7W52rJaOY0TpvbTQuNEaP6ZsibhHXIG33ySSP70F+6a3SFr5OPYNr1M79k94+57V7IUO7prGHyb25s7FG7nz7XXsqXQxbVQ3Eqwd+73lQ+pnoxUoHo0Ui0iKRyTFo1G0Y3Gg4yop1YbsDN95T03ORUREpHl2u71J0ij0vcPRsje29jeW09mytUhWVmqLzm+paI8fU9kDoOdzsHExvHUd1r2bSHv3Svj2eTjtHsgd0OQplx3fj+8qPSz4ajuP/Hcr/9m4hzmTD2dE905xuIC25ZD62WgFikcjxSKS4hFJ8WgU71goKdWGhCql8tNVKSUiIiLNy8vLo7y8HJ/Ph80WXMqVlpbicDhIS0tr8VhlZWURx8rKysjJyWnROHv2VB9I7+4WM4zgYjla48dVxmj49bs4lz9K0pK/YXz3CeYjY6gbehF1R/4fZmJKxOl/GN+Do3tnMfv1NXy7q4Zf/P1//OKIrlwxrgcp9o63pD+kfzZ+AsWjkWIRSfGIpHg0inYsQuP/mI73P1gbtqMytH1PlVIiIiLSvIEDB2Kz2VixYkW4SfnSpUsZMmQIFkvLtnQNHTqUpUuXMnnyZACKi4spLi5m6NChLRrHNA/ohnI/WbTHjxuLHdeIq3D3PZuUT2dj3/IOScsfxf7ta9SOuRlPn9PD+x8Mw+CsI/IpzHJy/4ebeWPNLhauKOKjjWVcd3wfxvfJjvPFxMch+7PxEykejRSLSIpHJMWjUbxj0bE3o7cxjdv3VCklIiIizXM6nUyaNInZs2ezatUqFi9ezLx58zj//POBYNWU2+0+oLHOOeccXnvtNRYuXMi6deu47rrrmDBhAt26dYvmJcj3BNK6UXXaP6n82Xz8ad2x1paQ9u7lpL9+LtbyjRHnZjgTuPmU/jz8yyEUZDjYXePlD6+t5frX11Ja44nTFYgAvjqspWvAr59DETlwSkq1EdVuH5VuHwD56aqUEhERkf2bMWMGhYWFTJs2jVtuuYXp06eH77A3duxY3nrrrQMaZ9iwYdx666089NBDnHPOOaSnpzNnzpxoTl1+gLfH8ew9531qj7wG02oncccndFpwIsmfzYF6V8S5Rx7WiefOH8G0Ud2wGvCfDWX86oklvLyyiIDe/pdYCvixr3uRzGfGk/nCyWQ9cSTJ/7sdS+XWeM9MRJrj95K48Q3SXjsPHhiBpWpbXKdjmKb+1wopK4veXsrs7NQfHH/drmqmPr2czKQE3rn86NafRBtyIPHoKBSLSIpHJMWjkWIRSfGIFO14hMaX/YvnGupQZancSsonN2Hf+j4A/pSuWE+8mXJ7T/zOHExHJlisAHy7u4bb39vA2pJqAIblpzHzxH70yEqK2/yjrU38bJgBLNU7CSTlgC2+Ox3iFY+E7R+T8t8/Y9uzFgDTsGKY/vDj3sPGU1d4Pt4ex4MlNp1j2sTPRhuieETqyPGwVmzGsfZZHOsWYqnb03DQTvmv3sSX2fQmGwfrQNdP6inVRjQ2OVeVlIiIiEhHF0jvTtXp80nc8h4pn9yEtXo7vHIZofvtmYYF05FFICmHI5NyeCk3h7VJDt7bYVBcnMoDT3/GuMEDOH3UEGwpmWBog8TBMlylJOxajm3XchJ2rcC2eyUWbxUBRyauIy7FPWQaZmLHSGBby9aS8r/bSdz+EQCBxDRcI66kbvA0End8inPNkyRu+yj84U/pgnvQebgHTSGQ3DnOsxfpQHxu7JvewrH2WRKLPg8f9ifl4Rn4K5LGXozfnwVxTNApKdVG7FA/KRERERH5Hm/PE9nbbSxJy/9O8tZ3CVSVYNTtwTADGHWlWOpKoeEN7xHACAuQ2PDkdcGPgGHDTMoi4MwhkJRDICkXMym74euGj4bHTHt6uLl6h1ZfR0LpKmy7VjQkoZZjrdnZ5DQTA4t7Lymf30nS8r9Td/hF1B1+IaYjI/ZzjgFLdRHJX96Nfd2LGJiYlgTqhkzDNfJqTEcwZertdTLeXidjqfwO55qncXzzPNaaYpK/vIekJffj7XkSdYXnU18wRj9rIlFi3bM+WBW1/iUsngog+GaG97DjcA86F2+P4zGsNpI6pUJZdVznqqRUG9F45z0lpURERERkHzYndaOuIfm0m9lbVo3p92HU7cXiCialLK5SLK7dWFxl4c+uimLM2lI6UY3F9EHtLqy1u370pUxLIoF9E1bObAJJuQ3Hgsksf3JnAildwZr4o+O1CwE/1vINweqnUAJq7/qIbWgQTED5M/tRn3sEvrxh1OcNw9+pN/ZNb5C05AFsFZtI/uo+nCsewz3kAlxHXILpzIrTRbUuw1NF0rKHcK78B0ZDI3N3nzOpPeo6Auk9mn1OIL0HtcfcSO2oP2Df9BbONU+RUPwV9k1vYd/0Fr6MXrgLp+Ie8ItwQktEDkK9C/vGRTjXPktCydLwYX9KPu5BU3AP+DWB1K5xnGDzlJRqIxrvvKfteyIiIiLyAyw2zORc/Mm5+H/gtMq6em79aD2frdlAtlFJH2ct5w20MyilLlhl5freh7cKI+DFWlOEtaboB6dgYhBIziOQ1g1/agH+1G4E0ho+p+bjT80Hq711r7uVWGpLwskn264V2HavwlJf0+Q8f1IevrwjqM8bhi9vGL7cw5vdnufp/ws8fc/Gvuktkpb+FduedSQtexDnqn9SVziVumGXEUjOi8WltT6/F+fqp0hacj8WdzkA3i6jqR1zI768YQc2hs2Bp/9kPP0nYy1bi3PN09jXv4StYjMp/72F5M/vxNP3LOoKfxMcU9VTIi1iK/0ax5pnsW94FYs3WPVkGla8PU8MVkV1Gx/uQdgWxTUp5fF4uOWWW3j33XdxOBxceOGFXHjhhc2e++mnn3LXXXexfft2hg4dyk033USvXr3Cj7/xxhvcf//9lJaWMnbsWG677TYyMzNjdSkHrbGnlCqlREREROTgpTsTmHnKYJYMKmDO4g28Wl7Hq0thQp8s/jixD7mp30sa+dzBaqvmElahY7W7sdSWYPg9WGtLsNaWkFD8VZPXDiatcgmkNiSt0roRSC3An1oQTGSldI1Nc3BvLQmlK7HtWhHuB2WtLWk6X1sS9bmHN1RABSuhAiktqCiwWPH0PQNPn5+RuOU9kpb8lYTSVSStfAzn6vm4B03BNeyKNlml0CzTJHHTm6R8NgdrVfAuer5Ofag9eibeHif+5MSRP3sQNePvoPbomdi/fQXn6iex7fkGx7oXcKx7gfrswbgHT8Xd72xIOHQb9YscLMNbjf3b13CsfZaE0lXh4/607tQNOgf3gF9hJufGcYYHLq5JqbvuuovVq1czf/58ioqKuP766+natSunnHJKxHkbNmzgsssu49JLL+WMM87gxRdfZNq0abz99tskJyezatUqZs2axS233MKAAQO4/fbbmTFjBo8++micrqxl6v0BdlUHy2BVKSUiIiIirWnkYRk8M3U4877YxpNf7eDDjXv4alsFV47ryeShXbCEEgw2B4G0AgJpBT88oGli1JVhrdqOtXoHluodwc8N31urt2P43FgbtgwmlCxpdhh/Ul5DdVVBMHm179epXcHWwnVxwId177eNzch3r8C691sMMxA5fcOCP7N/OPkU3IbXr3UqCQxLsKdSz5NI2PYhyUv+SkLJEpxfz8ex5lncA36Ba/iVBNK7H/xrRYmt6EtS/ncbCbuWAxBw5lA7+lrcA6e02h30zMSUYPKp8DfYdi3DufpJ7BvfIKFsNQkfXk/y//6Mp//PqSucij+rf6u8pki7Z5rYdi3HsfYZHBsWYfhcwcOWRDy9TsE96FzqC45pdze2MEwzPjdCdLlcHHXUUTz++OOMHj0agIcffpjPPvuMp556KuLcW2+9lW+//Zann34aANM0+dnPfsb555/PlClTuO6667BYLNx5550AFBcXc9xxx/Hee+/RrVu3A55TvG5nvHWvi1/8awnOBAsfTR+DcYiXrHbk23B+n2IRSfGIpHg0UiwiKR6Roh2PA72lcUcWrzVUR9JasdhYWsvt733L6uLgFo/Du6Yx66S+9MpKbqWZ0pC02tOYsAonr7ZjrQolrep+dBh/Um6wumqfKqtQpVUgLZ9sp5eqbz5pbEa+++vwL2kR46R0DW7Dyx0W/JxzOCS24vX+ENMkYef/SFryVxJ3/i94yLDi6Xc2rhFX4u/Up1VepjV+PqzlG0n+bA72Le8E52lLwjXst7iOuCwm8TLq9uJY9wLO1U+Fq7MAvF1H4y6ciqf3qQe0JVT/bkRSPCK1x3gY7grs376Mc+2z2PasCx/3deqDe9C5uPv/AtPZ8l1ibWX9FLdKqXXr1uHz+Rg2rHEv8ogRI3jkkUcIBAJYLI3Zve3bt3P44YeHvzcMg379+rFixQqmTJnCypUrueSSS8KPd+nSha5du7Jy5coWJaXiJdTkPD/decgnpEREREQkfvrkJPOPKUfw4ooiHv70O1YVVXHek8u4YFQ3/t/ow0i0tcI77IaBmZSNLykb8o5o+rhpYrjLsVZvj6iuslQFK66sVdsxfC6srt1YXbtJ2LVsvy+V9r3vAwkp+HKHRm7Di2c/J8OgvmAMlQVjsBV/RfKSv5K47UMc61/Evv4lPH3OwDVyOv6sgfGboquU5C/vw7H2WQzTj2lYcQ86B9eR/xfT2JnOTOqG/Za6Iy4lYcenOFc/SeKW90gs+oLEoi8IfJqFe+AU6grPI5B2WMzmJW1UwIfhrW74qAn2xPPWYHiqMOqDny3emn3Oafiod0F6LsmOLvtUZgaT36Yzu+30NDNNEoq/CPaK2vRm+AYDptWOp8/p1A06D1+XI9vOfA9C3JJSpaWldOrUicTExrt2ZGdn4/F4qKioiOgHlZ2dza5dkXcLKSkpIT09HYDdu3eTmxu5XzIrK4uSkqb7xX9ItP48Q+Pub/zGJueOQ+Fn6kf9WDw6EsUikuIRSfFopFhEUjwiRTseirMcaqwWg18Pz2d8nyzmvr+RTzfv5R+fb+O99aXMOqkfwwrSozsBw8B0ZuJzZkLu0KaPmyaGpwJr1fZwdVVoi2AokWWprwXDii97IPW5jc3I/Rm922xDX1+XI6k842lsu1aQtPQB7FvewbHxdRwbX8fT82RcI6/Gl3v4jw/UWupdJK14FOfyR4LxBDw9TqL26Bn4M/vGbh7fZ1io73Ys9d2OxVJTHLyt/dpnsdbuCt4BcNnDeA+bgHvw+Xi7T2yzf96yH2YAo742mCDyVH8vgdSQWGou2bTPYxZv9QFVW+5XKTS3Odi02huqMYM3bQj3wGu4mYOZlBP1RYFRtwfHuhdxrH0WW8Wm8HFf1kDqBp2Lp9/ZmI6MqM4h1uKWlKqrq4tISAHh771eb8TxU089ld/97necfvrpjBs3jkWLFvH111+Ht/253e5mx/r+OD8mKyu6pfn7G3+PJ7jPvW+XtA61PSDa8W5PFItIikckxaORYhFJ8YikeIi0TOc0B/dNKuT9b8u4+z8b2Vpex6XPr+TswzszfVwvUh1x+lXBMDAdnfA5OkFzSRrTxOKtICs3m4oqf7vZghPiyzuCqtP+ibVsLUlL/oZ905vYt7yDfcs7eA47DteRv8fXeUT0JhDw4fjmeZK+vBerazcA9blDqT3mRurzj47e6/4EgZQuuEZdi2vEVSRuXYxz9VMkbv8Y+7YPsG/7IHir+8LzqBs4pd00dW43TBP8boz6umASqd4V/Oz74e8Jne9zNRxzNXxd25BUqsGg9f7SmjYHZkIqAXsqZmLoIwUzMY1AYso+xxo+EpJIt1bjKtoYrtS0VG/HUhO8gYOtYhPskwyKeK0fSFoFUgsIJOX8tH5OZoCEHf/FsfZZ7JvfxgjUN1xbEu6+Z+IuPA9f7hGH7LtkcUtK2e32Jkmj0PcOR+SdOI499liuuOIKpk+fjt/vZ/To0Zx11lnU1NT84FhOZ8uaI+7ZE729lFlZqfsdf0NJJQBZditlZdWtP4E25sfi0ZEoFpEUj0iKRyPFIpLiESna8QiNL3IoMgyDE/rnMKp7Bn/7eAuvfV3CK6tK+GTTXv44sTfH9c1ue+0lGpJWJCYB7Xft7M8eRPUpj+DauyFYObXh1XCyxZs/BteRV1Pf9ejW+0XUNEnc+j7J/7sDW/m3wTmkHUbtUTfg6XNG2/6F15qAt9epeHudirViM441z+D45nmsNTtJ/uIukr66D0+vU3EPnoqvjSXW4ibgx1q1Fdve9bClGmf5Xmg2kdSQNPLWwvcTSd+7SUBrMi22cKIosE8iad/Pge8lmYLHGs6xp2EmpIA1oUWvaxhAdiquw763ZvB7sdQUhfveNd7AoeH7muIDTFrlN73raMPn7yetLLW7cHzzAo5vFkT0UavPHYp70Dl4+k7CTExp0fW1R3FLSuXl5VFeXo7P58NmC06jtLQUh8NBWtr3d4fD5ZdfzkUXXUR1dTVZWVlcffXV5Ofnh8cqKyuLOL+srIycnJwWzck0ierifn/j76gI9ZRydKhfLqId7/ZEsYikeERSPBopFpEUj0iKh8hPl+ZI4MaT+nHqwFzueG8D28rruH7RNxzbO4vrju9DXuqPN5iWn8af2ZfqE/9G7ZH/R9Kyh3Csf5HEnf8lced/qe8yitqRV1HfbfxBJY1su1eS/L8/k7jzMwAC9gxcR/6eusFTD6h5eFviz+hF7Zg/UTv6D9g3volz9ZMk7FqGY+MiHBsX4evUB0aeT0JSX3xpPQmkdm13dyNrEdPEcJVi27sO257gh3Xvemx712P43OHTDqZVvWm1YyYkYyYkBT/bnPt8n7TP8cbvsSU1PScxlUBCCqY9FayOtpUItSYSSO9BIL0H9c097vdiqSkO972zVG+PTFrVhiqtNkPF5mZfYt+kFYZBwvZPMEw/AIHEVDz9JlM36Fz8OYXRu842KG5JqYEDB2Kz2VixYgUjR44EYOnSpQwZMiSiyTnAG2+8wcqVK5k1axZZWVm43W6++OKL8N32hg4dytKlS5k8eTIQvPtecXExQ4c2s0e9jTFNk6KGRucFGS287a2IiIiISCsa0S2DZ88fwbwvtjH/y+18vGkPS7dX8LuxPfj50K5YLW3ol8hDTCCjJzUT78E18vckLX8Yx9oFJBR/Scai31CfewSukVfj7XFCi36Rt1RtI/nzuTg2vAYEfymuO/xCXCOuxLRHuXdYtNmceAb8As+AX2AtXYNzzVM41r+MrXwjvHcToaszrXb86T3wZ/TEn9ELf3ovfBm98Gf0bFuNrQ+Etxbb3vXY9q7DuqcxCWVx7232dNNqx5/ZD1tWd9ymPSJpZNqSMBMjE0n7JpbY5zz17aIhadWdQHr3/SSt6rHUFjckrJreddRSW9xs0qq+88hgr6g+p0NCUswupy2JW1LK6XQyadIkZs+ezR133MHu3buZN28ec+bMAYJVU6mpqTgcDnr06MGMGTM48sgj6devH3fffTddunTh2GOPBeCcc85h6tSpHHHEEQwZMoTbb7+dCRMmtIs775XVevH4AlgN6Kx3oEREREQkzuw2C5eP6cGJ/XO4491v+bq4mrv/s4m3v9nNzJP60Sf7YGou5McE0gqoGX8HrpFX4Vz+CM41T5OwewXpb/0/fFmDqB15Fd7ep/1g9Y/hLidpyQM4v34CI+DFxMDT/+fUjv4jgdT8GF5NbPhzCqmZcCe1x8zC8e3LpBR/iq90A9bKrcFEQEPl0PcFElMbElU9gp9DH+k9Me1Nd+/ETMCHtWJzsOoplHzauw5r1bZmTzcxgteQNQBfw4c/cwD+9B4YVivZ2anUlGnLf1RZEwikHbb/O0N+L2llcVfgPWwC/qz+sZ1nG2SYZvx+NOvq6pg9ezbvvvsuKSkpXHTRRVxwwQUA9O/fnzlz5oSrn1566SUeeughKioqOProo7n55psj7rj38ssv87e//Y3KykrGjBnDbbfdRqdOnVo0n7Io/UU1DMjOTm12/OU7Krn0+ZV0TXfw2sWjWv/F26AfikdHo1hEUjwiKR6NFItIikekaMcjNL7sXzzWUB1NvGLhD5i8tLKYhz/dQq3Xj81icO6IfH55RFc6pzl+fIAo6Ug/G4arjKSVj+H4en74Lnm+Tn1xjZiOp++ZYLE1xqOkFMeqJ0ha+gAWT7BvrbdgHLXHzMKXMzielxEzET8bfl+waqViC7aKzVgrN2Ot+A5rxWYs1Tt+sOF2wJmNP6MXvvSejVVWGT3xp/cAWyvtcDFNLDXFTSqfrOUbMQLN37TLn5SLP7N/Y/IpawC+Tv0gofk5daS/KwdC8WjUVtZPcU1KtTXxWFAtWl3Cre98y6jDMnjolzG8BWwc6R+CRopFJMUjkuLRSLGIpHhEaiuLqo5MSanoi3csdlV7uOv9jXy8aQ8AFgOO6ZnJpCFdGNMrE1uMt/XFOx7xYLjLca78J85V87B4qwDwpfegbviVeAacTfau/+B/7xas1TuCj2UNoOaYGw+6H1V7c8A/Gz431sqtWCu3YK3YHPyo3IK1Ykv4roTNMTEIpHRtTFI1VFb5M3riT+2238bbhqcS6571TXo/hZKHTV7HloQvq3+46imUhDKdWS0JR4f8u/JDFI9GbWX9FLftexK0Q/2kRERERKSNy0u1c89Zg/h40x6eXbqTZTsq+XTzXj7dvJeclETOGNyZswZ3pmt6/KqnDnWmoxOu0X+g7ohLcX49H+fKx7BVfkfqB38g5ZMbg0kWwJ/cmdrR1+Hp/3P1AvohNgf+rP7Nbp8yvNVYK79rTFZVbA4mqyo2Y/FWYa3ZibVmJ+z4JOJ5psWGP+2wcKIKiy3YdHzPOqw1Rc1OwzSs+DN6N1Y9ZQ3Al9mfQFq3Q7tBu0gDJaXibGdFHQAFGfoPXERERETaLsMwGN8nm/F9svlur4vXvi7hjTW7KK3xMu/zbfzr822M7tGJsw/vwrG9MrFZ9Qt1NJj2NFwjp+M6/EKca54iafmjWOpKITGV2uFX4Bp6UettL+ugzMRUfDlD8OUM+d4DJoZ7bzhB1bglcAvWyi0YPje2huPN8ad0xZfZf5/eTwPxd+rd7u6AKNKalJSKs50NlVL5qpQSERERkXaiR2YSV4/vxeVjevDRpj28sqqYr7ZV8Pl35Xz+XTmZSQmcMbgzk4Z01o6AaElMpm7Yb6kbMg37jk9JGzSeurpEfqBNkhwsw8B0ZuFzZuHrMhLPvo+ZASw1JRHbAY2AF19mf3yZA/Bn9W//dzwUiQIlpeJsR0XD9j2VOouIiIhIO5Nos3Bi/xxO7J/Djoo6Xv26hEWrS9jrqmf+l9uZ/+V2jjwsg7MP78L43lkk2lQ91epsTrw9T4TkVKirjvdsOi7DQiC1K4HUrtQXjIn3bETaDSWl4qjG46Oirh6AfG3fExEREZF2rCDDyZXjevLbY7rz8ea9vLqqmM+/K+erbRV8ta2CDGcCpxfmMWlIZ7pnJsV7uiIi0gYoKRVHOxuqpDo5E0hO1B+FiIiIiLR/NquFiX2zmdg3m6JKN6+tDlZPldZ4eXrJDp5esoPhBelMOrwzE/vmYFf1lIhIh6VMSBztqFSTcxERERE5dHVNd3D5mB5ccnR3/rdlL6+sKuZ/W/aybEcly3ZUcq9jE6cOClZP9c5Ojvd0RUQkxpSUiqNQPyk1ORcRERGRQ5nNYnBs7yyO7Z3FrmoPr68u4bWvS9hV7WHBsp0sWLaToV3TmHR4Z07ol4MjwRrvKYuISAwoKRVHOyoaKqXU5FxEREREOoi8VDuXHN2dC0cfxudby3l1VTGfbNrDyqIqVhZVce8Hmzh1YLB6ql9uSrynKyIiUaSkVBztrGy4854qpURERESkg7FaDMb0zGRMz0zKajwsWrOLV78uoajSzcIVRSxcUURh51TOPrwzJ/bPJSlR1VMiIocaJaXiaGdDpVS+KqVEREREpAPLTrHz/0YfxrRR3fhqawWvfl3Mhxv3sKakmjUl1fzlw82cPCCXSYd3ZmBearynKyIirURJqTip9wcoqfYAanQuIiIiIgJgMQxG9+jE6B6d2Ovy8uaaXbyyqpjtFW5eXlXMy6uKGZCbwqTDO3POmF7xnq6IiBwkJaXipLjKQ8AEh81CVnJivKcjIiIiItKmZCYlMvXIbvxmZAHLdlTyyqpi/rOhjHW7a7hz8Ubuen8j/XNTGFaQzvCCDIYVpJHmSIj3tEVEpAWUlIqTUJPz/AwHhmHEeTYiIiIiIm2TYRiM6JbBiG4ZVLjqeeubXbz2dQmb97j4ZlcN3+yq4dmlOzGAPjnJDC9IZ3i3DIbnp5ORpCSViEhbpqRUnOyoaGhynq4m5yIiIiIiByIjKYFzRxRw3sgCfAkJLF61k2XbK1m2o4Lv9taxobSWDaW1PL+8CIBeWUmNSaqCdO1QEBFpY5SUipOdlY2VUiIiIiIi0jKd0x2cMjCXkwfkAlBW62X5jkqWba9g2Y5KNu9xhT9eXFkMQPdOTkY0JKiGd0snJ8Uez0sQEenwlJSKk52hSqkMVUqJiIiIiBys7ORETuyfw4n9cwAod3lZvrMqnKTaWFrL1vI6tpbX8fKqYJKqW4aD4QUZDO+WzvCCdDqn6Q1jEZFYUlIqTnaEKqXS9R+fiIiIiEhr65SUyMS+2Uzsmw1AZV09K3ZWsmxHJcu2V/JtaQ3bK9xsryjhtdUlAHRNszMsVElVkE5+uvq/iohEk5JScWCapiqlRERERERiKN2ZwPg+2YzvE0xS1Xh8wSTV9mCiat2uaoqqPBSt2cWba3YBkJuSGO5HNaJbBt10kyIRkValpFQc7Kn14vYFsBjQJU372EVEREREYi3FbmNsryzG9soCoNbrY1VRVThJtaakmt01Xt7+Zjdvf7MbCG4RDPWjGl6QQY9Mp5JUIiIHQUmpOAjdea9zqp0EqyXOsxERERERkeREG0f3yOToHpkA1NX7g0mqHZUs317B6pJqymq9vLu+lHfXlwKQmZTA6O6dmNA3m6N7dMKZYI3nJYiItDtKSsVBuJ+Utu6JiIiIiLRJzgQro7t3YnT3TgC46/2sKaluqKSq4Oviava66vn3N7v59ze7sdssHN2jExP6ZDOudyZpjoQ4X4GISNunpFQcNPaTUpNzERERaTmPx8Mtt9zCu+++i8Ph4MILL+TCCy9s9ty1a9dy88038+2339KnTx9uueUWBg8eHH585MiRVFdXRzxn2bJlJCcnR/UaRNobR4KVEd0yGNEtA+iO1xfg6+IqPt60hw83lFFU5eHDjXv4cOMerBaDEQXpTOibzYQ+WeSkqGWHiEhzlJSKgx2VDUmpdFVKiYiISMvdddddrF69mvnz51NUVMT1119P165dOeWUUyLOc7lcXHrppZxxxhnceeedPPfcc1x22WW89957JCUlsWvXLqqrq1m8eDEOR+ObZUlJSbG+JJF2J9FmCSepfj++F9+W1vLBhjI+3FjGpjIXX26r4MttFdz1/kaGdEllQp9sJvTN5rBO+h1ARCRESak42FkR2r6nSikRERFpGZfLxcKFC3n88ccpLCyksLCQDRs28MwzzzRJSr311lvY7Xauu+46DMNg1qxZfPzxx7z99ttMnjyZTZs2kZOTQ7du3eJ0NSKHBsMw6J+bQv/cFH47pgfbyuv4sCFB9XVxdfjjgU+20Ds7iQl9sjmubzb9cpLVKF1EOjQlpeIg1OhclVIiIiLSUuvWrcPn8zFs2LDwsREjRvDII48QCASwWBpvorJy5UpGjBgR/qXXMAyGDx/OihUrmDx5Mhs3bqRnz54xvwaRQ91hnZycP6ob54/qxu5qDx81bPFbur2CTWUuNpVt45+fb6Nrmr1hi182h3dNw2pRgkpEOhYlpWKs1uujvK4eUKWUiIiItFxpaSmdOnUiMTExfCw7OxuPx0NFRQWZmZkR5/bp0yfi+VlZWWzYsAGATZs2UVdXx9SpU9myZQsDBw5k5syZLU5URavQIzSuCkkUi+9rT/HIS7Pzq2Fd+dWwrlTW1fPJ5r18uKGMz74rp6jKw7NLd/Ls0p1kJiUwvk8WE/pkc+RhGSTaDvwu3e0pHtGmWERSPCIpHo2iHYsDHVdJqRgLVUllOBNIsSv8IiIi0jJ1dXURCSkg/L3X6z2gc0Pnbd68mcrKSq655hpSUlJ4/PHHueCCC3jzzTdJSUk54DllZaX+lEtpM+O3J4pFpPYWj2ygd7dMLhjfB5fXx8fflvLOml0s/mYXe131vLKqhFdWlZBqt3HcgFxOLuzMhP45JB/g7w3tLR7RpFhEUjwiKR6N4h0LZUViLNRPSnfeExERkZ/Cbrc3ST6Fvt+3WfkPnRs675///Cf19fXhO+3dc889jB8/ng8++IAzzjjjgOe0Z081ptniS/lRhhFcLEdr/PZEsYh0qMRjZOcURnZO4boJPVm6vYIPNgTv3ren1svrK4t4fWURiVaD0T06cVyfbI7tnUVGUkKTcQ6VeLQGxSKS4hFJ8WgU7ViExv8xSkrF2M6GO+/lpyspJSIiIi2Xl5dHeXk5Pp8Pmy24lCstLcXhcJCWltbk3LKysohjZWVl5ObmAsGqqX0rqex2OwUFBezatatFczJNorq4j/b47YliEelQiYfNYmF090xGd8/kuuP78HVRFR9u3MOHG8vYUeHmk017+WTTXqwGDCtIZ0KfbMb3yaJzWuTvFIdKPFqDYhFJ8YikeDSKdywOfKOytIpwk/MMNTkXERGRlhs4cCA2m40VK1aEjy1dupQhQ4ZENDkHGDp0KMuXL8dsWG2apsmyZcsYOnQopmlywgkn8PLLL4fPd7lcbN26lV69esXkWkSkKYthMDQ/navH9+LlC4/k2fOHc+nR3embk4zfhCXbK7nng02c8fiXTHtmOf/6Yhtb9rjwB/Qbtoi0P6qUirEdDdv3VCklIiIiP4XT6WTSpEnMnj2bO+64g927dzNv3jzmzJkDBKumUlNTcTgcnHLKKdx7773cfvvtTJkyhQULFlBXV8epp56KYRhMmDCBBx54gPz8fDIzM/nrX/9K586dGT9+fJyvUkQgeMfMvjkp9M1J4ZJjurOjoi5YQbWhjFVFVawtqWZtSTUPf/odFiPYt7ZTUgKdkhLJbPg6Mymx4XPD8aTg8aQEa/jOnCIi8aKkVIztqFSllIiIiBycGTNmMHv2bKZNm0ZKSgrTp0/npJNOAmDs2LHMmTOHyZMnk5KSwqOPPsrNN9/MCy+8QP/+/XnsscdISkoC4I9//CM2m41rr72WmpoajjrqKB577DGsVms8L09E9qMgw8lvRhbwm5EFlNV6+XhjGR9s3MOSbRX4AiZ7XfXsddUDrh8dy26z0OlHEleZzsSGJFcCCVZtshGR1meYpnZShpSVRa/BV3Z2KiW7Khlz/6f4TXjrstHkpNhb/8XagVA8ohXv9kSxiKR4RFI8GikWkRSPSNGOR2h82b9ox14/64rF9ykekfyBABannY07ytlbW8/eOi/lDQmqcpe34XPw6z2uejy+QItfI9VuCyaofiCRlZmUSF6qnaTE+CW29bMRSfGIpHg0aivrJ1VKxVBxlQe/GXxXIjs58cefICIiIiIi8iNsVgvZaQ4suSkH9MtlXb2fva6miauKuuYTWX4Tqj0+qj0+tpXX/ej46Q4bXdIcdE6z0zXdQec0B11S7XRJd9AlzU6q3aatgyICKCkVU/v2k9I/wiIiIiIiEg/OBCv56U7y03+8pUjANKl2+4IJrB+owNrrqqes1kut10+l20elu4Z1u2uaHTM50UrnNDtd0hwNH/bw585pDjKTEvT7kkgHoaRUDO1UPykREREREWlHLIZBujOBdGcCPUj60fNrPD6Kq9wUVXooqXJTXOWhuMpNcZWbkioP5XX11Hr9bCpzsams+d5XdpuFzqn271Vb2emSGvyck2LHalHSSuRQoKRUDO2oCCWldOc9ERERERE59KTYbeE7Bjanrt5PSUOiqqTKTVFVY/KqpMpNaY0Xjy/A1vI6tu5nq6DVYpCXam9SYdWlIYGVlpEczUsUkVakpFQM7bt9T0REREREpKNxJljpmZVEz6zmq67q/QF2VYeqqzwUV7oprm5IXFW62VXjxR8wKap0U1TpBiqbjGEYkJ0cbLreOdVObmowadU51R48lmank1NbBEXaAiWlYihUKZWv7XsiIiIiIiJNJFgtFGQ499vyxB8wKa3xUFLloahhS2Boe2Co2srrNymt8VJa42V1cXWz4yRag9VWeWmOcPKqc6qdvDQ7nVMdcb+LoEhHoaRUjJimyc7KYKVUgSqlREREREREWsxqMYJVT2kOjiC9mTNMcNj5Zuseiqs87GqostpVHfraw55aL16/yfYKN9sbCgeak+awBRNXoaTVPgmsvDQ7OcmJ2KyW6F2sSAegpFSMlNZ4qKsPYDGgq5JSIiIiIiIirc4wDLJT7RidUxmYl9rsOfX+ALsbqq1CyapQwqqkOpjAqvH4qXL7qHL72FBa2+w4lvA2wWAD9sbkVfDrggwnKXb9yi3yQ/Q3JEa27w3eWSIv1U6CsukiIiIiIiJxkWC1kJ/uJD99/21Vajy+YKKq2sOuhkqrkn2SV7uqPfgCJrtrvOyu8fJ1cfPjdO/kZHCXVAq7pDG4Syp9s5NVXSWyDyWlYmTrnmBSSv2kRERERERE2rYUu40Uu43e2c3fyS9gmux11TebsCpp2DK411Ufvovgm2t3A2C3Weifm8LgLqkMbkhUdU61q+m6dFhKSsVIOCmlrXsiIiIiIiLtmsUwyE5OJDs5kcIuzZ9T4apnTUk1q4urWF1SzdqSaqrcPlYVVbGqqArYCUBmUkI4QVXYOZVBnVO17U86DP2kx8i2hu17anIuIiIiIiJy6MtISmBMr0zG9MoEgje/2lZex+riYKJqTUk135bWstdVz8eb9vDxpj0AGECPrCQGd04NV1T1yk7GZlE1lRx6lJSKka17gs3x9ndrUxERERERETl0GYZB98wkumcm8bPCPADc9X7W765pqKiqZk1xFUVVHrbscbFlj4tFa3YB4LBZGNg5NZyoKuySRl6qPZ6XI9IqlJSKkXClVIYqpURERERERAQcCVaG5qczND89fGxPrTeYoCqpYnVxcNtfrdfP8h2VLN9RGT4vJyWRws6NvakG5qWSlGiNyby9vgC1Xh81Hj+1Xh+1Xn/4632P1XoaPnv9mKZJgtVCgtXAZrVgsxgkWIzGY5bg8YR9PkecGz5maTi38esEq0GCxYKtYZzQmOFzLYb6drVRSkrFQK3XR1mNF1CllIiIiIiIiOxfVnIi4/tkMb5PFhBsqv7dXldDJVVw69+mslpKa7x8uHEPH24MbvuzGNA7O7khURWspuqVlRQxtqchmVQbkUyKTCrte2zfpFL4mNdHvd+MeVwOVih5lZvmICcpgc5pDjqn2umS5iAvreFzqh27TXdHjCUlpWKgqNINQLrTpoZ1IiIiIiIicsAshkGvrGR6ZSVz5uDOANTV+/lmVyhJFUxU7a7xsqG0lg2ltbz6dQkAyYlWslLsVNXVRyWZlJRgJdluJTnRSnKijRR78HNyopVke/Bzit1GUqIVA/AFTOr9AXx+M/x1fcNnf8Ck3t/weMPXvkAg4nPo6/Bz9xkn8nPT6/QFgudu3eNi6x6AyibnQLDxfChh1TnNTuc0B132+TrdYVPVVStShiQGdlQEk1IF6aqSEhERERERkYPjTLAyvCCD4QUZ4WOlNZ6GBFVw619o219tQyuZfYWSSSmJtoikUiiJtG9S6fvHUhrOTUq0Ym2jzddN08QfMMMJqvpwUiuA12pl3fZySqo8lFS7Ka7ysKvKQ3GVG7cvwF5XPXtd9awtqW52bGeChc6poeoqO51THQ0Jq2C1VU5yIjarqq0OlJJSMbCjog6AfPWTEhERERERkSjISbFzXF87x/XNBsAfCG77szkT8dV5w8klZ0LbTSa1FsMI9pyyWcGRsO9xyM5OpXdaIub3iqlM06TS7aOkyt2QsAomqnZVeyiu8lBS5Wavq566+gBb9rrY0kyyD4LbKLOTE+mS5ghXV0VuE7STnKhUTIgiEQPhSiklpURERERERCQGrBaDPjnJZGenUlZW3SQJI5EMwyDDmUCGM4EBeanNnuPxBdhV7dkncRWstCppOLar2kO932R3jZfdNV5WFjX/Wil2K2mOBNLsNlIcNlLtNlLtwaRhqt1GmsMW/jo14hwbzgTLIbV9UEmpGAhVSmn7noiIiIiIiEj7ZLdZOKyTk8M6Nf+7fcA02euqDyetvl9pVVLtocodvENhjcfPfnJWP8hqMZoksVIbklhp+3wdTmjZraQ6go+l2G3YbW0rqRXXpJTH4+GWW27h3XffxeFwcOGFF3LhhRc2e+57773HfffdR0lJCQMGDODGG2+ksLAwPM5dd93FW2+9BcCJJ57IDTfcQFJSUrNjxZoqpUREREREREQObRbDIDs5kezkRAZ3af6cWq+P0movVR4f1R4fNe7g52qPjxqPryFpFToWvOthtdtHlceHPxDslVVRV09FXf1PmmOC1WhIVtnok5fKzIm9SXcm/PgToySuSam77rqL1atXM3/+fIqKirj++uvp2rUrp5xySsR5GzZs4Nprr+XWW29l+PDhPPHEE1x22WW89957OJ1OHnzwQb788ksee+wxTNPkhhtu4L777uPGG2+M05VFstssJCVa6Z2dHO+piIiIiIiIiEicJCfaSM5qeSrGNE3cvgDV7sYEViiZFTpW7fZHHP9+kitgQr3fDDdz31Zex2+Gd2VIR0xKuVwuFi5cyOOPP05hYSGFhYVs2LCBZ555pklS6r///S99+vRh0qRJAFxzzTU888wzbNy4kSFDhvDRRx/x61//miFDhgBwzjnn8Pzzz8f6kvbr0V8fjiPFiTPg1z5eEREREREREWkRwzBwJlhxJljJTbW3+PmmaeKq91Md2j7o9dElJ5W8xPhu5YvbfQrXrVuHz+dj2LBh4WMjRoxg5cqVBAKBiHMzMjLYuHEjS5cuJRAI8PLLL5OSksJhhx0Wfvydd96hsrKSyspK3n33XQYOHBjT6/khnZIS6ZbZNrYSioiIiIiIiEjHYhgGyYk2Oqc56JOTzLCCdAbnp8e9v1TcKqVKS0vp1KkTiYmJ4WPZ2dl4PB4qKirIzMwMHz/ttNP4z3/+w7nnnovVasVisfDoo4+Snp4OwHXXXcf06dMZPXo0AP369ePvf/97bC9IREREREREREQOWNySUnV1dREJKSD8vdfrjTheXl5OaWkpN910E0OHDuW5555jxowZvPLKK2RlZbFt2za6dOnCnXfeic/n49Zbb+XOO+/kz3/+c4vmFK0EYWjcNtTgPq4Uj0aKRSTFI5Li0UixiKR4RIp2PBRnERERkeiIW1LKbrc3ST6Fvnc4Iu9Sd88999CvXz/OO+88AG677TZOPfVUXnrpJc4991xmzZrFE088wdChQwG44447+M1vfsNVV11Fbm7uAc8pKyv1YC4p7uO3N4pHI8UikuIRSfFopFhEUjwiKR4iIiIi7UvcklJ5eXmUl5fj8/mw2YLTKC0txeFwkJaWFnHumjVrmDp1avh7i8XCgAEDKCoqYvPmzbhcLgYMGBB+fNCgQQQCAUpKSlqUlNqzpzoqjcgNI7hQjtb47Y3i0UixiKR4RFI8GikWkRSPSNGOR2h8EREREWldcUtKDRw4EJvNxooVKxg5ciQAS5cuZciQIVgskf3Xc3Nz2bRpU8SxLVu2MGTIkHDSaePGjRQWFgKwefNmAAoKClo0J9Mkqov7aI/f3igejRSLSIpHJMWjkWIRSfGIpHiIiIiItC9xu/ue0+lk0qRJzJ49m1WrVrF48WLmzZvH+eefDwSrptxuNwC/+tWveOGFF3j11VfZunUr99xzD0VFRZx99tl07tyZcePG8ac//YnVq1fz9ddf86c//Ymf/exnEc3SRURERERERESk7YhbpRTAjBkzmD17NtOmTSMlJYXp06dz0kknATB27FjmzJnD5MmTOe2006itreXRRx+lpKSEgQMHMn/+fLKysgC49957ufPOO7n00ksxDIPjjz+e66+/Pp6XJiIiIiIiIiIiPyCuSSmn08ncuXOZO3duk8fWr18f8f0vf/lLfvnLXzY7Tnp6OnPmzInKHEVEREREREREpPXFbfueiIiIiIiIiIh0XEpKiYiIiIiIiIhIzCkpJSIiIiIiIiIiMaeklIiIiIiIiIiIxJySUiIiIiIiIiIiEnNKSomIiIiIiIiISMwpKSUiIiIiIiIiIjFni/cE2hLDiO640Rq/vVE8GikWkRSPSIpHI8UikuIRKdrxUJx/nNZQ0adYRFI8IikejRSLSIpHJMWjUVtZPxmmaZrRmYKIiIiIiIiIiEjztH1PRERERERERERiTkkpERERERERERGJOSWlREREREREREQk5pSUEhERERERERGRmFNSSkREREREREREYk5JKRERERERERERiTklpUREREREREREJOaUlBIRERERERERkZhTUirKPB4PM2fOZOTIkYwdO5Z58+bFe0pxs2vXLq666ipGjRrFuHHjmDNnDh6PJ97TahMuvfRSbrjhhnhPI668Xi+33HILRx55JMcccwz33XcfpmnGe1pxUVxczGWXXcbw4cOZOHEiTzzxRLynFBder5fTTz+dL774Inxs+/btXHDBBRxxxBGcdtppfPrpp3GcYWw1F48VK1YwZcoUhg0bxsknn8zChQvjOMPYai4eIdXV1YwbN46XX345DjOT1qD1UyStoZqn9ZPWT9+nNVSQ1lCNtH6K1BbXT0pKRdldd93F6tWrmT9/PjfffDMPPvggb7/9drynFXOmaXLVVVdRV1fHM888w1/+8hc++OAD7r///nhPLe7efPNNPvroo3hPI+7+/Oc/87///Y9//vOf3Hvvvbzwwgs8//zz8Z5WXPz+978nKSmJl19+mZkzZ3L//ffz3nvvxXtaMeXxeLjmmmvYsGFD+JhpmlxxxRVkZ2fz0ksvcdZZZ3HllVdSVFQUx5nGRnPxKC0t5ZJLLmHUqFG88sorXHXVVdx22218+OGH8ZtojDQXj33dfffd7N69O8azktak9VMjraGap/VTkNZPkbSG0hpqX1o/RWqr6ydbzF+xA3G5XCxcuJDHH3+cwsJCCgsL2bBhA8888wynnHJKvKcXU5s3b2bFihX897//JTs7G4CrrrqKuXPncv3118d5dvFTUVHBXXfdxZAhQ+I9lbiqqKjgpZde4l//+heHH344ABdeeCErV65kypQpcZ5dbFVWVrJixQpuu+02evToQY8ePRg3bhyfffYZJ554YrynFxMbN27k2muvbfJO7+eff8727dtZsGABSUlJ9O7dm88++4yXXnqJ6dOnx2m20be/eCxevJjs7GyuueYaAHr06MEXX3zBokWLmDBhQhxmGhv7i0fIkiVL+Pzzz8nJyYnxzKS1aP0USWuoprR+CtL6KZLWUFpD7Uvrp0htef2kSqkoWrduHT6fj2HDhoWPjRgxgpUrVxIIBOI4s9jLycnhH//4R3gxFVJTUxOnGbUNc+fO5ayzzqJPnz7xnkpcLV26lJSUFEaNGhU+dumllzJnzpw4zio+HA4HTqeTl19+mfr6ejZv3syyZcsYOHBgvKcWM19++SWjR49u8k7vypUrGTRoEElJSeFjI0aMYMWKFTGeYWztLx6hLTzfd6j/u7q/eECwJP1Pf/oTN910E4mJiXGYnbQGrZ8iaQ3VlNZPQVo/RdIaSmuofWn9FKktr59UKRVFpaWldOrUKeIPNjs7G4/HQ0VFBZmZmXGcXWylpaUxbty48PeBQICnn36ao446Ko6ziq/PPvuMJUuWsGjRImbPnh3v6cTV9u3byc/P59VXX+WRRx6hvr6eyZMnc/nll2OxdKzcud1u56abbuK2227jySefxO/3M3nyZH75y1/Ge2oxc+655zZ7vLS0lNzc3IhjWVlZlJSUxGJacbO/eBQUFFBQUBD+fs+ePbz55puH7DueIfuLB8AjjzzCoEGDGDt2bAxnJK1N66dIWkNF0vqpkdZPkbSG0hpqX1o/RWrL6yclpaKorq6uSaYx9L3X643HlNqMu+++m7Vr1/Liiy/Geypx4fF4uPnmm7nppptwOBzxnk7cuVwutm7dyoIFC5gzZw6lpaXcdNNNOJ1OLrzwwnhPL+Y2bdrEcccdx//7f/+PDRs2cNttt3H00Udz5plnxntqcbW/f1M7+r+nAG63m+nTp5Odnc2vf/3reE8nLjZu3MiCBQt4/fXX4z0VOUhaP/2wjryG0vopktZPTWkN1TytoZqn9VPbWD8pKRVFdru9yV/00Pcd+T/Su+++m/nz5/OXv/yFfv36xXs6cfHggw8yePDgiHc+OzKbzUZNTQ333nsv+fn5ABQVFfHcc891uEXVZ599xosvvshHH32Ew+FgyJAh7Nq1i7///e8dfkFlt9upqKiIOOb1ejv0v6cAtbW1/O53v+O7777j2Wefxel0xntKMWeaJjfeeCNXXXVVky1O0v5o/bR/HX0NpfVTJK2fImkNtX9aQzWl9VPbWT8pKRVFeXl5lJeX4/P5sNmCoS4tLcXhcJCWlhbn2cXHbbfdxnPPPcfdd9/NySefHO/pxM2bb75JWVlZuF9GaLH9zjvvsHz58nhOLS5ycnKw2+3hBRVAz549KS4ujuOs4mP16tV07949YpEwaNAgHnnkkTjOqm3Iy8tj48aNEcfKysqalKN3JDU1NVx88cVs27aN+fPn06NHj3hPKS6KiopYvnw569evZ+7cuUDwXeGbb76Zt956i3/84x9xnqG0hNZPzdMaSuun79P6KZLWUPunNVQkrZ+C2sr6SUmpKBo4cCA2m40VK1YwcuRIINiQcMiQIR1yn/eDDz7IggULuO+++zrk3XP29dRTT+Hz+cLf33PPPQD84Q9/iNeU4mro0KF4PB62bNlCz549geDdhvZdZHUUubm5bN26Fa/XGy6z3rx5c8Te945q6NChPPbYY7jd7vCCc+nSpYwYMSLOM4uPQCDAlVdeyY4dO3jqqafo3bt3vKcUN3l5ebz77rsRx6ZOncrUqVM7/Lvj7ZHWT01pDRWk9VMkrZ8iaQ21f1pDNdL6qVFbWT91zP/ZY8TpdDJp0iRmz57NqlWrWLx4MfPmzeP888+P99RibtOmTTz88MNccskljBgxgtLS0vBHR5Sfn0/37t3DH8nJySQnJ9O9e/d4Ty0uevXqxYQJE5gxYwbr1q3jk08+4bHHHuOcc86J99RibuLEiSQkJHDjjTeyZcsW/vOf//DII48wderUeE8t7kaNGkWXLl2YMWMGGzZs4LHHHmPVqlX84he/iPfU4uLFF1/kiy++4M9//jNpaWnhf1O/X57fEdhstoh/U7t3747NZiMrK4u8vLx4T09aSOunSFpDNdL6KZLWT5G0hto/raEaaf3UqK2sn1QpFWUzZsxg9uzZTJs2jZSUFKZPn85JJ50U72nF3Pvvv4/f7+fvf/87f//73yMeW79+fZxmJW3JPffcw2233cY555yD0+nkvPPO65CLiNTUVJ544gluv/12fvGLX5CZmcnll1/eYZsv7stqtfLwww8za9YsJk+eTPfu3XnooYfo2rVrvKcWF++88w6BQIDLLrss4vioUaN46qmn4jQrkdah9VMjraHkh2j91EhrqP3TGqqR1k9tj2GaphnvSYiIiIiIiIiISMei7XsiIiIiIiIiIhJzSkqJiIiIiIiIiEjMKSklIiIiIiIiIiIxp6SUiIiIiIiIiIjEnJJSIiIiIiIiIiISc0pKiYiIiIiIiIhIzCkpJSIiIiIiIiIiMaeklIiIiIiIiIiIxJwt3hMQEWktEydOZOfOnc0+9uSTTzJ69OiovO4NN9wAwJ133hmV8UVERESiSWsoEYkXJaVE5JAyc+ZMTjvttCbH09PT4zAbERERkfZBaygRiQclpUTkkJKamkpOTk68pyEiIiLSrmgNJSLxoJ5SItJhTJw4kSeeeIIzzjiDI444gksvvZTS0tLw45s2beKiiy5i+PDhjBs3jgcffJBAIBB+/LXXXuOUU05h6NChTJkyhbVr14Yfq6mp4f/+7/8YOnQoEyZMYNGiRTG9NhEREZFo0RpKRKJFSSkR6VAeeOABLr74Yp5//nnq6uqYPn06AHv37uXcc88lNzeXhQsXcvPNN/P000/z5JNPAvDJJ58wa9Yspk2bxuuvv87gwYO57LLL8Hq9ALz33nsUFhbyxhtvcOqppzJz5kyqq6vjdp0iIiIirUlrKBGJBsM0TTPekxARaQ0TJ06ktLQUmy1yZ3LXrl158803mThxIieccAIzZ84EYPv27ZxwwgksWrSIzz//nHnz5rF48eLw85977jkeeughPv30U6688kpSUlLCjTi9Xi9/+ctfuPDCC7n33nv57rvvWLBgAQDV1dWMHDmSF154gaFDh8YwAiIiIiItpzWUiMSLekqJyCHlqquu4qSTToo4tu8Ca/jw4eGvu3XrRkZGBps2bWLTpk0UFhZGnDts2DBKS0upqqpiy5YtTJkyJfxYYmIi119/fcRYIampqQB4PJ7WuzARERGRKNIaSkTiQUkpETmkZGVl0b179/0+/v13AP1+PxaLBbvd3uTcUC8Ev9/f5HnfZ7VamxxTIaqIiIi0F1pDiUg8qKeUiHQo69atC3+9detWqqur6d+/Pz179mTNmjXU19eHH1++fDmZmZlkZGTQvXv3iOf6/X4mTpzI0qVLYzp/ERERkXjQGkpEokFJKRE5pFRXV1NaWtrkw+VyAfDkk0/y/vvvs27dOmbOnMmYMWPo0aMHZ5xxBl6vl5tuuolNmzaxePFiHnjgAc455xwMw2Dq1Km8/vrrvPLKK2zdupU5c+ZgmiaFhYVxvmIRERGRg6c1lIjEg7bvicgh5Y477uCOO+5ocvzqq68G4Oyzz+a+++6jqKiI8ePHc8sttwCQkpLCP/7xD26//XYmTZpEZmYm06ZN47LLLgPgyCOP5Oabb+ahhx6itLSUwYMH88gjj+BwOGJ3cSIiIiJRojWUiMSD7r4nIh3GxIkTufLKK5k8eXK8pyIiIiLSbmgNJSLRou17IiIiIiIiIiISc0pKiYiIiIiIiIhIzGn7noiIiIiIiIiIxJwqpUREREREREREJOaUlBIRERERERERkZhTUkpERERERERERGJOSSkREREREREREYk5JaVERERERERERCTmlJQSEREREREREZGYU1JKRERERERERERiTkkpERERERERERGJOSWlREREREREREQk5v4/fAUfQwTX9TYAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 对比",
   "id": "386fd0bd9c555a66"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T05:49:53.047646Z",
     "start_time": "2025-06-12T05:47:59.016881Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "# 导入PyTorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "# 设置随机种子以便结果可复现\n",
    "np.random.seed(42)\n",
    "torch.manual_seed(42)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(42)\n",
    "\n",
    "# 设置文件路径\n",
    "base_path = r\"C://Users/30979//Desktop//mnist_dataset\"\n",
    "train_path = os.path.join(base_path, \"mnist_train.csv\")\n",
    "test_path = os.path.join(base_path, \"mnist_test.csv\")\n",
    "\n",
    "# 检查是否有可用的GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"使用设备: {device}\")\n",
    "\n",
    "# 1. 加载数据\n",
    "print(\"正在加载数据...\")\n",
    "train_data = pd.read_csv(train_path)\n",
    "test_data = pd.read_csv(test_path)\n",
    "\n",
    "print(f\"训练集大小: {train_data.shape}\")\n",
    "print(f\"测试集大小: {test_data.shape}\")\n",
    "\n",
    "# 2. 数据预处理\n",
    "print(\"\\n数据预处理...\")\n",
    "# 分离特征和标签\n",
    "X_train = train_data.drop(columns=['label']).values.astype('float32')\n",
    "y_train = train_data['label'].values.astype('int64')\n",
    "X_test = test_data.drop(columns=['label']).values.astype('float32')\n",
    "y_test = test_data['label'].values.astype('int64')\n",
    "\n",
    "# 归一化像素值到0-1范围\n",
    "X_train = X_train / 255.0\n",
    "X_test = X_test / 255.0\n",
    "\n",
    "# 准备不同模型的输入数据\n",
    "# MLP输入: 展平的28x28=784维向量\n",
    "X_train_mlp = X_train.reshape(-1, 784)\n",
    "X_test_mlp = X_test.reshape(-1, 784)\n",
    "\n",
    "# LSTM输入: [样本数, 时间步, 特征] = [N, 28, 28]\n",
    "X_train_lstm = X_train.reshape(-1, 28, 28)\n",
    "X_test_lstm = X_test.reshape(-1, 28, 28)\n",
    "\n",
    "# CNN输入: [样本数, 通道, 高度, 宽度] = [N, 1, 28, 28]\n",
    "X_train_cnn = X_train.reshape(-1, 1, 28, 28)\n",
    "X_test_cnn = X_test.reshape(-1, 1, 28, 28)\n",
    "\n",
    "print(f\"MLP训练数据形状: {X_train_mlp.shape}\")\n",
    "print(f\"LSTM训练数据形状: {X_train_lstm.shape}\")\n",
    "print(f\"CNN训练数据形状: {X_train_cnn.shape}\")\n",
    "\n",
    "# 可视化一些样本\n",
    "plt.figure(figsize=(10, 5))\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    plt.imshow(X_train[i].reshape(28, 28), cmap='gray')\n",
    "    plt.title(f\"Label: {y_train[i]}\")\n",
    "    plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 3. 定义MLP模型\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_size=784, hidden_size1=512, hidden_size2=256, hidden_size3=128, num_classes=10):\n",
    "        super(MLP, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, hidden_size1)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.dropout1 = nn.Dropout(0.2)\n",
    "        \n",
    "        self.fc2 = nn.Linear(hidden_size1, hidden_size2)\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.dropout2 = nn.Dropout(0.2)\n",
    "        \n",
    "        self.fc3 = nn.Linear(hidden_size2, hidden_size3)\n",
    "        self.relu3 = nn.ReLU()\n",
    "        self.dropout3 = nn.Dropout(0.2)\n",
    "        \n",
    "        self.fc4 = nn.Linear(hidden_size3, num_classes)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu1(x)\n",
    "        x = self.dropout1(x)\n",
    "        \n",
    "        x = self.fc2(x)\n",
    "        x = self.relu2(x)\n",
    "        x = self.dropout2(x)\n",
    "        \n",
    "        x = self.fc3(x)\n",
    "        x = self.relu3(x)\n",
    "        x = self.dropout3(x)\n",
    "        \n",
    "        x = self.fc4(x)\n",
    "        return x\n",
    "\n",
    "# 4. 定义LSTM模型\n",
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_size=28, hidden_size=128, num_classes=10, dropout=0.2):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        \n",
    "        self.lstm1 = nn.LSTM(input_size, hidden_size, batch_first=True, dropout=dropout)\n",
    "        self.lstm2 = nn.LSTM(hidden_size, hidden_size//2, batch_first=True, dropout=dropout)\n",
    "        self.fc1 = nn.Linear(hidden_size//2, 32)\n",
    "        self.fc2 = nn.Linear(32, num_classes)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        batch_size = x.size(0)\n",
    "        \n",
    "        h0_1 = torch.zeros(1, batch_size, self.hidden_size).to(x.device)\n",
    "        c0_1 = torch.zeros(1, batch_size, self.hidden_size).to(x.device)\n",
    "        \n",
    "        out, _ = self.lstm1(x, (h0_1, c0_1))\n",
    "        out = self.dropout(out)\n",
    "        \n",
    "        hidden_size_2 = self.hidden_size // 2\n",
    "        h0_2 = torch.zeros(1, batch_size, hidden_size_2).to(x.device)\n",
    "        c0_2 = torch.zeros(1, batch_size, hidden_size_2).to(x.device)\n",
    "        \n",
    "        out, _ = self.lstm2(out, (h0_2, c0_2))\n",
    "        out = self.dropout(out)\n",
    "        \n",
    "        out = out[:, -1, :]\n",
    "        \n",
    "        out = self.relu(self.fc1(out))\n",
    "        out = self.dropout(out)\n",
    "        out = self.fc2(out)\n",
    "        return out\n",
    "\n",
    "# 5. 定义CNN模型\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(1, 32, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2)\n",
    "        )\n",
    "        \n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(32, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2)\n",
    "        )\n",
    "        \n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2)\n",
    "        )\n",
    "        \n",
    "        self.fc1 = nn.Linear(128 * 3 * 3, 128)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc2 = nn.Linear(128, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc1(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "# 6. 训练和评估模型的函数\n",
    "def train_and_evaluate_model(model, model_name, X_train, y_train, X_test, y_test, input_type):\n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(f\"开始训练 {model_name} 模型...\")\n",
    "    \n",
    "    # 创建数据集和数据加载器\n",
    "    if input_type == \"mlp\":\n",
    "        X_train_tensor = torch.tensor(X_train)\n",
    "        X_test_tensor = torch.tensor(X_test)\n",
    "    elif input_type == \"lstm\":\n",
    "        X_train_tensor = torch.tensor(X_train)\n",
    "        X_test_tensor = torch.tensor(X_test)\n",
    "    elif input_type == \"cnn\":\n",
    "        X_train_tensor = torch.tensor(X_train)\n",
    "        X_test_tensor = torch.tensor(X_test)\n",
    "    \n",
    "    y_train_tensor = torch.tensor(y_train)\n",
    "    y_test_tensor = torch.tensor(y_test)\n",
    "    \n",
    "    train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "    test_dataset = TensorDataset(X_test_tensor, y_test_tensor)\n",
    "    \n",
    "    batch_size = 128\n",
    "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "    \n",
    "    # 分割训练集和验证集\n",
    "    validation_split = 0.2\n",
    "    train_size = int(len(train_dataset) * (1 - validation_split))\n",
    "    val_size = len(train_dataset) - train_size\n",
    "    train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size])\n",
    "    \n",
    "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
    "    \n",
    "    # 定义损失函数和优化器\n",
    "    model = model.to(device)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.Adam(model.parameters())\n",
    "    \n",
    "    # 记录训练开始时间\n",
    "    start_time = time.time()\n",
    "    \n",
    "    # 训练历史记录\n",
    "    train_losses = []\n",
    "    train_accuracies = []\n",
    "    val_losses = []\n",
    "    val_accuracies = []\n",
    "    \n",
    "    # 早停参数\n",
    "    patience = 5\n",
    "    best_val_loss = float('inf')\n",
    "    counter = 0\n",
    "    num_epochs = 15\n",
    "    \n",
    "    # 训练模型\n",
    "    for epoch in range(num_epochs):\n",
    "        # 训练阶段\n",
    "        model.train()\n",
    "        train_loss = 0.0\n",
    "        train_correct = 0\n",
    "        train_total = 0\n",
    "        \n",
    "        for inputs, labels in train_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            train_loss += loss.item() * inputs.size(0)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            train_total += labels.size(0)\n",
    "            train_correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        train_loss = train_loss / len(train_loader.dataset)\n",
    "        train_accuracy = train_correct / train_total\n",
    "        train_losses.append(train_loss)\n",
    "        train_accuracies.append(train_accuracy)\n",
    "        \n",
    "        # 验证阶段\n",
    "        model.eval()\n",
    "        val_loss = 0.0\n",
    "        val_correct = 0\n",
    "        val_total = 0\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for inputs, labels in val_loader:\n",
    "                inputs, labels = inputs.to(device), labels.to(device)\n",
    "                \n",
    "                outputs = model(inputs)\n",
    "                loss = criterion(outputs, labels)\n",
    "                \n",
    "                val_loss += loss.item() * inputs.size(0)\n",
    "                _, predicted = torch.max(outputs.data, 1)\n",
    "                val_total += labels.size(0)\n",
    "                val_correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        val_loss = val_loss / len(val_loader.dataset)\n",
    "        val_accuracy = val_correct / val_total\n",
    "        val_losses.append(val_loss)\n",
    "        val_accuracies.append(val_accuracy)\n",
    "        \n",
    "        print(f'Epoch {epoch+1}/{num_epochs}:')\n",
    "        print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_accuracy:.4f}')\n",
    "        print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_accuracy:.4f}')\n",
    "        \n",
    "        # 早停检查\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            counter = 0\n",
    "        else:\n",
    "            counter += 1\n",
    "            if counter >= patience:\n",
    "                print(f'Early stopping at epoch {epoch+1}')\n",
    "                break\n",
    "    \n",
    "    # 计算训练时间\n",
    "    training_time = time.time() - start_time\n",
    "    print(f\"\\n{model_name} 模型训练完成！耗时: {training_time:.2f} 秒\")\n",
    "    \n",
    "    # 加载最佳模型并评估\n",
    "    model.eval()\n",
    "    test_loss = 0.0\n",
    "    test_correct = 0\n",
    "    test_total = 0\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in test_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            test_loss += loss.item() * inputs.size(0)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            test_total += labels.size(0)\n",
    "            test_correct += (predicted == labels).sum().item()\n",
    "            \n",
    "            all_preds.extend(predicted.cpu().numpy())\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "    \n",
    "    test_loss = test_loss / len(test_loader.dataset)\n",
    "    test_accuracy = test_correct / test_total\n",
    "    \n",
    "    print(f\"测试集准确率: {test_accuracy:.4f}\")\n",
    "    print(f\"测试集损失: {test_loss:.4f}\")\n",
    "    \n",
    "    # 计算混淆矩阵\n",
    "    conf_mat = confusion_matrix(all_labels, all_preds)\n",
    "    \n",
    "    # 保存训练历史\n",
    "    history = {\n",
    "        'train_loss': train_losses,\n",
    "        'train_acc': train_accuracies,\n",
    "        'val_loss': val_losses,\n",
    "        'val_acc': val_accuracies,\n",
    "        'test_acc': test_accuracy,\n",
    "        'test_loss': test_loss,\n",
    "        'training_time': training_time,\n",
    "        'confusion_matrix': conf_mat,\n",
    "        'all_preds': all_preds,\n",
    "        'all_labels': all_labels\n",
    "    }\n",
    "    \n",
    "    return history, model_name\n",
    "\n",
    "# 7. 运行并比较三种模型\n",
    "models = [\n",
    "    (MLP(), \"MLP\", X_train_mlp, y_train, X_test_mlp, y_test, \"mlp\"),\n",
    "    (LSTMModel(), \"LSTM\", X_train_lstm, y_train, X_test_lstm, y_test, \"lstm\"),\n",
    "    (CNN(), \"CNN\", X_train_cnn, y_train, X_test_cnn, y_test, \"cnn\")\n",
    "]\n",
    "\n",
    "all_histories = []\n",
    "\n",
    "for model, name, X_train, y_train, X_test, y_test, input_type in models:\n",
    "    history, model_name = train_and_evaluate_model(model, name, X_train, y_train, X_test, y_test, input_type)\n",
    "    all_histories.append(history)\n",
    "\n",
    "# 8. 对比结果可视化\n",
    "\n",
    "# 8.1 准确率对比\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 绘制各模型的训练和验证准确率\n",
    "for i, history in enumerate(all_histories):\n",
    "    model_name = [\"MLP\", \"LSTM\", \"CNN\"][i]\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history['train_acc'], label=f'{model_name} Train')\n",
    "    plt.plot(history['val_acc'], label=f'{model_name} Validation')\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('模型准确率对比')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "# 绘制各模型的测试准确率\n",
    "test_accs = [h['test_acc'] for h in all_histories]\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.bar([\"MLP\", \"LSTM\", \"CNN\"], test_accs, color=['blue', 'green', 'red'])\n",
    "for i, v in enumerate(test_accs):\n",
    "    plt.text(i, v + 0.01, f'{v:.4f}', ha='center')\n",
    "plt.title('测试集准确率对比')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim(0.8, 1.0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 8.2 损失对比\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 绘制各模型的训练和验证损失\n",
    "for i, history in enumerate(all_histories):\n",
    "    model_name = [\"MLP\", \"LSTM\", \"CNN\"][i]\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history['train_loss'], label=f'{model_name} Train')\n",
    "    plt.plot(history['val_loss'], label=f'{model_name} Validation')\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('模型损失对比')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "# 绘制各模型的测试损失\n",
    "test_losses = [h['test_loss'] for h in all_histories]\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.bar([\"MLP\", \"LSTM\", \"CNN\"], test_losses, color=['blue', 'green', 'red'])\n",
    "for i, v in enumerate(test_losses):\n",
    "    plt.text(i, v - 0.01, f'{v:.4f}', ha='center')\n",
    "plt.title('测试集损失对比')\n",
    "plt.ylabel('Loss')\n",
    "plt.ylim(0, 0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 8.3 训练时间对比\n",
    "training_times = [h['training_time'] for h in all_histories]\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.bar([\"MLP\", \"LSTM\", \"CNN\"], training_times, color=['blue', 'green', 'red'])\n",
    "for i, v in enumerate(training_times):\n",
    "    plt.text(i, v + 5, f'{v:.2f}s', ha='center')\n",
    "plt.title('模型训练时间对比')\n",
    "plt.ylabel('Training Time (seconds)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 8.4 性能指标表格\n",
    "results = pd.DataFrame({\n",
    "    '模型': [\"MLP\", \"LSTM\", \"CNN\"],\n",
    "    '测试集准确率': [h['test_acc'] for h in all_histories],\n",
    "    '测试集损失': [h['test_loss'] for h in all_histories],\n",
    "    '训练时间(秒)': [h['training_time'] for h in all_histories]\n",
    "})\n",
    "\n",
    "print(\"\\n模型性能对比表:\")\n",
    "print(results)\n",
    "\n",
    "# 8.5 各模型混淆矩阵\n",
    "plt.figure(figsize=(18, 6))\n",
    "for i, history in enumerate(all_histories):\n",
    "    model_name = [\"MLP\", \"LSTM\", \"CNN\"][i]\n",
    "    plt.subplot(1, 3, i+1)\n",
    "    sns.heatmap(history['confusion_matrix'], annot=True, fmt='d', cmap='Blues', \n",
    "                xticklabels=range(10), yticklabels=range(10))\n",
    "    plt.xlabel('Predicted Label')\n",
    "    plt.ylabel('True Label')\n",
    "    plt.title(f'{model_name} Confusion Matrix')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "8d3e506d87d9d1d8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cuda\n",
      "正在加载数据...\n",
      "训练集大小: (60000, 785)\n",
      "测试集大小: (10000, 785)\n",
      "\n",
      "数据预处理...\n",
      "MLP训练数据形状: (60000, 784)\n",
      "LSTM训练数据形状: (60000, 28, 28)\n",
      "CNN训练数据形状: (60000, 1, 28, 28)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\an\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:123: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "开始训练 MLP 模型...\n",
      "Epoch 1/15:\n",
      "Train Loss: 0.3954, Train Acc: 0.8805\n",
      "Val Loss: 0.1589, Val Acc: 0.9513\n",
      "Epoch 2/15:\n",
      "Train Loss: 0.1423, Train Acc: 0.9575\n",
      "Val Loss: 0.1205, Val Acc: 0.9641\n",
      "Epoch 3/15:\n",
      "Train Loss: 0.0987, Train Acc: 0.9701\n",
      "Val Loss: 0.0947, Val Acc: 0.9728\n",
      "Epoch 4/15:\n",
      "Train Loss: 0.0791, Train Acc: 0.9756\n",
      "Val Loss: 0.0887, Val Acc: 0.9732\n",
      "Epoch 5/15:\n",
      "Train Loss: 0.0672, Train Acc: 0.9787\n",
      "Val Loss: 0.0845, Val Acc: 0.9750\n",
      "Epoch 6/15:\n",
      "Train Loss: 0.0527, Train Acc: 0.9834\n",
      "Val Loss: 0.0859, Val Acc: 0.9760\n",
      "Epoch 7/15:\n",
      "Train Loss: 0.0461, Train Acc: 0.9850\n",
      "Val Loss: 0.0859, Val Acc: 0.9788\n",
      "Epoch 8/15:\n",
      "Train Loss: 0.0421, Train Acc: 0.9864\n",
      "Val Loss: 0.0813, Val Acc: 0.9789\n",
      "Epoch 9/15:\n",
      "Train Loss: 0.0336, Train Acc: 0.9894\n",
      "Val Loss: 0.0843, Val Acc: 0.9806\n",
      "Epoch 10/15:\n",
      "Train Loss: 0.0330, Train Acc: 0.9897\n",
      "Val Loss: 0.0854, Val Acc: 0.9792\n",
      "Epoch 11/15:\n",
      "Train Loss: 0.0303, Train Acc: 0.9896\n",
      "Val Loss: 0.0956, Val Acc: 0.9759\n",
      "Epoch 12/15:\n",
      "Train Loss: 0.0296, Train Acc: 0.9906\n",
      "Val Loss: 0.0858, Val Acc: 0.9797\n",
      "Epoch 13/15:\n",
      "Train Loss: 0.0257, Train Acc: 0.9918\n",
      "Val Loss: 0.0889, Val Acc: 0.9782\n",
      "Early stopping at epoch 13\n",
      "\n",
      "MLP 模型训练完成！耗时: 26.71 秒\n",
      "测试集准确率: 0.9798\n",
      "测试集损失: 0.0828\n",
      "\n",
      "==================================================\n",
      "开始训练 LSTM 模型...\n",
      "Epoch 1/15:\n",
      "Train Loss: 1.0269, Train Acc: 0.6531\n",
      "Val Loss: 0.3175, Val Acc: 0.9075\n",
      "Epoch 2/15:\n",
      "Train Loss: 0.2859, Train Acc: 0.9243\n",
      "Val Loss: 0.1889, Val Acc: 0.9463\n",
      "Epoch 3/15:\n",
      "Train Loss: 0.1793, Train Acc: 0.9540\n",
      "Val Loss: 0.1223, Val Acc: 0.9681\n",
      "Epoch 4/15:\n",
      "Train Loss: 0.1350, Train Acc: 0.9653\n",
      "Val Loss: 0.1116, Val Acc: 0.9689\n",
      "Epoch 5/15:\n",
      "Train Loss: 0.1077, Train Acc: 0.9724\n",
      "Val Loss: 0.0865, Val Acc: 0.9765\n",
      "Epoch 6/15:\n",
      "Train Loss: 0.0910, Train Acc: 0.9771\n",
      "Val Loss: 0.0727, Val Acc: 0.9813\n",
      "Epoch 7/15:\n",
      "Train Loss: 0.0781, Train Acc: 0.9803\n",
      "Val Loss: 0.0650, Val Acc: 0.9832\n",
      "Epoch 8/15:\n",
      "Train Loss: 0.0705, Train Acc: 0.9814\n",
      "Val Loss: 0.0708, Val Acc: 0.9809\n",
      "Epoch 9/15:\n",
      "Train Loss: 0.0611, Train Acc: 0.9835\n",
      "Val Loss: 0.0719, Val Acc: 0.9811\n",
      "Epoch 10/15:\n",
      "Train Loss: 0.0536, Train Acc: 0.9860\n",
      "Val Loss: 0.0642, Val Acc: 0.9832\n",
      "Epoch 11/15:\n",
      "Train Loss: 0.0477, Train Acc: 0.9877\n",
      "Val Loss: 0.0627, Val Acc: 0.9843\n",
      "Epoch 12/15:\n",
      "Train Loss: 0.0446, Train Acc: 0.9878\n",
      "Val Loss: 0.0720, Val Acc: 0.9823\n",
      "Epoch 13/15:\n",
      "Train Loss: 0.0420, Train Acc: 0.9889\n",
      "Val Loss: 0.0565, Val Acc: 0.9850\n",
      "Epoch 14/15:\n",
      "Train Loss: 0.0382, Train Acc: 0.9898\n",
      "Val Loss: 0.0585, Val Acc: 0.9865\n",
      "Epoch 15/15:\n",
      "Train Loss: 0.0362, Train Acc: 0.9900\n",
      "Val Loss: 0.0559, Val Acc: 0.9863\n",
      "\n",
      "LSTM 模型训练完成！耗时: 52.47 秒\n",
      "测试集准确率: 0.9877\n",
      "测试集损失: 0.0511\n",
      "\n",
      "==================================================\n",
      "开始训练 CNN 模型...\n",
      "Epoch 1/15:\n",
      "Train Loss: 0.2914, Train Acc: 0.9073\n",
      "Val Loss: 0.0738, Val Acc: 0.9760\n",
      "Epoch 2/15:\n",
      "Train Loss: 0.0689, Train Acc: 0.9781\n",
      "Val Loss: 0.0479, Val Acc: 0.9842\n",
      "Epoch 3/15:\n",
      "Train Loss: 0.0502, Train Acc: 0.9849\n",
      "Val Loss: 0.0343, Val Acc: 0.9889\n",
      "Epoch 4/15:\n",
      "Train Loss: 0.0389, Train Acc: 0.9881\n",
      "Val Loss: 0.0359, Val Acc: 0.9882\n",
      "Epoch 5/15:\n",
      "Train Loss: 0.0326, Train Acc: 0.9905\n",
      "Val Loss: 0.0270, Val Acc: 0.9906\n",
      "Epoch 6/15:\n",
      "Train Loss: 0.0264, Train Acc: 0.9920\n",
      "Val Loss: 0.0323, Val Acc: 0.9892\n",
      "Epoch 7/15:\n",
      "Train Loss: 0.0249, Train Acc: 0.9920\n",
      "Val Loss: 0.0307, Val Acc: 0.9907\n",
      "Epoch 8/15:\n",
      "Train Loss: 0.0192, Train Acc: 0.9941\n",
      "Val Loss: 0.0280, Val Acc: 0.9917\n",
      "Epoch 9/15:\n",
      "Train Loss: 0.0163, Train Acc: 0.9949\n",
      "Val Loss: 0.0291, Val Acc: 0.9912\n",
      "Epoch 10/15:\n",
      "Train Loss: 0.0162, Train Acc: 0.9948\n",
      "Val Loss: 0.0305, Val Acc: 0.9912\n",
      "Early stopping at epoch 10\n",
      "\n",
      "CNN 模型训练完成！耗时: 29.91 秒\n",
      "测试集准确率: 0.9927\n",
      "测试集损失: 0.0264\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:395: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:395: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:395: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:395: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:395: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:395: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:395: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:395: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:395: UserWarning: Glyph 35797 (\\N{CJK UNIFIED IDEOGRAPH-8BD5}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:395: UserWarning: Glyph 38598 (\\N{CJK UNIFIED IDEOGRAPH-96C6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35797 (\\N{CJK UNIFIED IDEOGRAPH-8BD5}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38598 (\\N{CJK UNIFIED IDEOGRAPH-96C6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:424: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:424: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:424: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:424: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:424: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:424: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:424: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:424: UserWarning: Glyph 35797 (\\N{CJK UNIFIED IDEOGRAPH-8BD5}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:424: UserWarning: Glyph 38598 (\\N{CJK UNIFIED IDEOGRAPH-96C6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": 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z5sxRmzZtAhHmcTWIaiBJ2l+QHuBIAAA1SWpqilJTU5Se7vn74bPPPlJqaoqmT58qSZo+fapGj77xmH2NHn2jzjmnk6vPHj3O1uDBl+iNN16TzWbzaP/444/olluuO2pfGzas03nndfHYt/Fo8a9du1qSNHjwJZo374ujtktL26fU1BSlpVXujzNr1qzSzp07JEnz5n2hwYMvqdR5AAAAODnUmKLUyYCZUgCAY7FYLFq6dJHH8cWLF8owjCr1ddVVwzV37nzNnTtfH330hf7973v1wQfvatasNz3a9ulzgTZu/OWohafvv1+gjh1TFB8fX+mxp017W717961SvMdy5523KDs7S5LUu3dfTZv2tlf6BQAAQHCgKOVF5UWpA4X7ZXfaAxwNAKAm6dChk5YsWex2LD8/Txs3/qIWLVpWqS+r1aq4uHjFxcWrfv0Epaaep379+mvRoh882p511jmKiYnR4sXu7zmdTi1c+J369r2gSmPHxsYqLMz7T5gNCwtXbGys1/sFAABAzUVRyosSIhJkNsyyOW3KKjr+UggAQO3Svft5WrdurfLz81zHli1bog4dzvTKHolms1khIZ4P1bVYLOrVq49HwWrDhnU6fPiQevbsrfz8PE2cOEEXX9xXPXueo6uuulyLFy886jh/X75ns9n0wguT1b9/Tw0adKGWLVvi1nbHju26++7R6tv3PJ1//rm69dZRruV65Uv17rjjZk2fPtVj+d7OnTt09923q1+/Hho4cIDefHOaHA6HpLIljhMmPKxnn31K/fr10IABvTV79szqJQ4AAAABQ1HKiywmixLCEyXxBD4A8BunUyot8O+X01nlMJs2ba74+AQtX/6T69jixQvVvXvPE/r4drtdP/+8Rt98M1+pqT2O2qZPnwu0bt0aHTp00HXs+++/1TnndFNUVJRefPE57dmzSy+8MEWzZn2oDh06atKkx1VaWnrcsadPn6qlS3/U008/r8cff1offfS+6z2Hw6H7779LyckN9NZb7+rVV2fIbrfr1VdfkiTXUr0nn5ysK68c7tbvwYMHddttoxQfH6/XX39L99xzvz7++APNmfOeq80PPyxQaGioZsx4R8OGjdCrr76s3bt3VSl3AAAACCzPP6nihCRFJCutcJ/SC9LUvt6ZgQ4HAE5uTqfqfjJIIemr/TpsafJZOjjoE6mKe0F1736eli5drN69+6qkpESrVi3X3Xffp2+++bpK/cycOUPvvjtLklRSUiKz2aw+fS7wKO6U69Cho+Li4rVkyWJddNG/5HA4tHDh97rrrnslSWee2UlDhw5T06bNJUlXXnm1vvjiM2VnZykxMemofTqdTn3xxWcaPfrfOvPMTpKkO+64W/fe+29JUnFxsQYOvFyDBg2R1WqVJA0YcLHefbesGFW+VC86OsZjpti3385XWFi47rvvIVksFp166mnKysrUm29O0//93zBJUp06dXTbbf+W2WzW1VeP1Ntvv6XNm3/TKac0qVIuAQAAEDgUpbwsyZosiZlSAOA3VSwMBVJqag89/PD9stlsWrNmpZo2ba7Y2HpV7mfQoMG6/PL/kyRZLCGKi4tTSEjIMdsbhqHevftp0aLvddFF/9L69T+rqKhQXbumSpL6979IP/64UJ9//ql27dqpLVs2S5JrudzRHDx4UAcP5rjth9WqVVvX91arVQMHDtb8+V9p8+ZftXv3Tm3ZskX16lX8eXft2qGWLVvLYvnrNuWMMzooKytLubm5kqTk5IYym82u9yMiImS3ez59EAAAADUXRSkvS46gKAUAfmMYZTOWbIX+HddirVYxrH37MyWV7ee0ePEinXdez2oNHxMTo0aNGlfpnL59L9DNN1+ngoJ8ff/9AvXocb7CwsIkSU88MU6//LJB/ftfqIEDBysuLl4333xtpfp1/m0p498LYwUFBbrhhhGqU6euUlPPU58+F2j37p167713KuwzNDTU45jDYXf7798LVkeLBQAAADUfRSkvc82UKqAoBQB+YRhSyIlvFO4PFotFXbt209Kli7Vs2WINHz7Db2OffnorJSc30E8/LdOPPy7UQw+Nk1T2BMBvv52v119/S61bl810+umnsg3Lj1fkqVu3rurVi9PmzZvUvHkLSdLvv292vf/zz2uUmZmhmTPfdxWQVq1aXqnC0SmnNNHChd/LZrO5zt248RfVrRurmJg6Vf7sAAAAqJkoSnlZUkQDScyUAgAcXffuPTRx4mNq0KChGjRoeNQ2ubmHtXz5Mrdj8fH1XcWf6urT5wK9886bcjod6tTpLElSaGiYwsOtWrjwe9WtG6vdu3fp+eefkaTjbnRuGIYuu2yI3nhjqhITkxUdHa2XX37e9X6dOnVUWFioH39cqFat2mj16pX6+OMPFRkZ5WpjtVq1Y8cfOv30Vm599+s3QNOnv67Jk5/UVVeN0J49uzRjxlQNGjRERhAt1wQAAMDxUZTysqQjy/cOFB2Q3WGT2USKAQB/6dKlq2w2m7p3P/qT8iTpjz+2acyYO9yO9es3QI8++vgJjd2nzwWaPn2qBg8e6tqPKSQkRI8++pimTPmPPvrofSUnN9TIkddp2rRX9fvvm9WkyanH7G/EiOtUVFSkceMelNls1rXX3qDnn58kSTrjjPa65ppReu65SSopKVGzZs1199336+mnH1dGxgHVr5+gwYOH6r//fUl//rlXzZuf7uo3IiJSzz33kl588Tldd90w1a0bqyFDrtTw4ZVbUggAAIDgYDhr2QYMmZm51XmSd4UMQ4qPj9aBjEPqP7+XSh2lerfnx64iVW1Tng9f5TsYkRNP5MQd+fD0z5yUlpYoKytNcXHJCgnx3HeotrBYTLLZjr0JeW3jjXwc79oqvw5rM34u+Q+/CxBMuF4RLLhW/a+y908mP8RSq5gMkxLDyx6fnVa4L8DRAAAAAAAA1EwUpXwgMaKsKMVm5wAAAAAAAEdHUcoHkq1sdg4AAAAAAHA8FKV8IMlato8URSkAAAAAAICjoyjlA+Wbm7N8DwAAAAAA4OgoSvkAM6UAAAAAAACOj6KUDyRFlO0plVmUoVJHaYCjAQAAAAAAqHkoSvlAbGiswkxhcsqpA4X7Ax0OAAAAAABAjUNRygcMw/hrXymW8AEAAAAAAHiwBDqAk1WiNVm78nYqrWBfoEMBAARYamqKXnrpNXXqlHLU9/fu3aNXX31Ja9asVmlpiU47rZn+7/+uUt++/SVJo0ffqHXr1h6z/zlzPteMGa/r66+/1PXX36Rrr73B7f38/DxdfHFfxcXF66OPvnB7Ly1tn4YM+dcx+z7zzE6aMuX1yn5USdLatat1xx03a8mS1VU6DwAAALULRSkfST6y2fl+ZkoBAI6jqKhId9xxs849t7teeWWaQkPDtGLFT3riiXEKCQlRz569NXHiMyotLduj8L333tGmTRv0xBOTXX3UrRsrSbJYLFqyZLFHUWrZsiWy2WxHHT8hIVFz5853vb7hhpEaOvRq9e7dV5IUEhJS5c/Url0Htz4BAACAo6Eo5SOJR5bvpRVQlAIAHNvq1StUUFCgMWMecB1r1Kixtm7dos8//0w9e/ZWTEwd13tWq1UWS4ji4uI9+urQoaPWrl2tjIwDql8/wXX8xx8XqW3bdsrMzPA4x2w2u/VlMpkUFRV11P4rKyTk6PEBAAAAf8eeUj6SZGVPKQBAxQzDpMLCAm3c+Ivb8ZtuGq3773+oSn0lJCSqRYuWWrp0setYSUmJVq78Samp51U7xiefHK8nnxyvkSOv1MUX99WePbu1Y8d23X33aPXte57OP/9c3XrrKO3cuUNS2fK91NSypYppafuUmpqiRYu+1xVXXKrzzz9X9933bx0+fKja8QAAAODkwEwpH0mmKAUAfuF0OlVkL/LrmOHmcBmG4ZW+UlK6qHHjJrrllut0xhnt1aXLOerSpavatj2jWv11795DS5Ys1sCBgyVJa9as1GmnNVW9enEnFOf//jdPEyc+q7i4ODVs2EhDhw7SWWedrXvueUB5eXl6/vlJevXVlzRp0gtHPf/tt9/U+PFPyumUHnjgbr333ju66abbTigmAAAABDeKUj5S/vS97OIsFduLFWYOC3BEAHDycTqdumP5zdqU80vFjb3ojNj2evGcV71SmAoLC9Mrr7yhWbPe1Pfff6vp06dq+vSpOv30lpow4Sk1bnxKlfpLTe2hWbPeVGFhoaxWqxYvXqTu3XudcJytWrVxzbYqLCzUwIGXa9CgIbJarZKkAQMu1rvvvn3M86+//ia1aVNWaOvXr782b/71hGMCAABAcGP5no/EhNSR1Rwhic3OAcCXDHlnxlIgxcTE6Lbb7tTHH3+pt99+XzfccIvS0tL08MP3V7mvFi1OV716cVq58ic5HA4tXbpYPXqceFEqOTnZ9b3VatXAgYM1f/5Xeuqpx3TLLdfppZeel8PhOOb5jRo1dn0fERF5zI3XAQAAUHswU8pHDMNQkjVJO/K2K70wXadEnRrokADgpGMYhl4859WgXr73+eefKjIyyvW0u6ZNm6tp0+Zq2bK1xoy5Qzk5OYqNja1Sn6mpZUv46tWLU2xsrBo2bKT1638+oThDQ/+a8VtQUKAbbhihOnXqKjX1PPXpc4F2796p995755jn//Mpfk6n84TiAQAAQPCjKOVDSRENyopSPIEPAHzGMAxZLdZAh1Ft27dv04YN69SrV2+ZTH9NYI6KilZoaKgiIyOr3Gf37j00btyDqls3Vuedd+KzpP7p55/XKDMzQzNnvi+LpexWYtWq5RSaAAAAUCUUpXwoyZokSUov3BfgSAAAgfbbb5tUUlLiduzMMztpyJArNX/+V3rooXt15ZXDFR9fXzt2bNfUqVM0aNAQhYaGVnmsDh06ym63a+7cT/Tf/77urY/gUqdOHRUWFurHHxeqVas2Wr16pT7++ENFRkZ5fSwAAACcvChK+VBSRANJUnpheoAjAQAE2quvvuxx7P33P1WjRo31yivT9cYbr+nBB+9VXl6ukpKSdfHFl+rKK4dXayyLxaKuXbvpl1/Wq0WLlicauoczzmiva64Zpeeem6SSkhI1a9Zcd999v55++nFlZBzw+ngAAAA4ORnOWjbXPjMzV774xIYhxcdHu/X/Y/oijVs7Vi3rtNar3aZ7f9Aa7Gj5qO3IiSdy4o58ePpnTkpLS5SVlaa4uGSFhFR9BtHJwmIxyWY79qbitY038nG8a6v8OqzN+LnkP/wuQDDhekWw4Fr1v8reP/H0PR9Kjih7UhFP3wMAAAAAAHBHUcqHkqxlRamDJQdVaCsIcDQAAAAAAAA1B0UpH4oKiVaUpWy6GvtKAQAAAAAA/IWilI8lRRx5Al8BS/gAAAAAAADKUZTyscQjS/jS2VcKAAAAAADAhaKUjyW7ilL7AhwJAAAAAABAzUFRyseSjjyBL72APaUAAAAAAADKUZTysSRrA0ks3wMAAAAAAPg7ilI+lsTyPQAAAAAAAA8UpXys/Ol7uaW5yivNC3A0AIBASE1N0dq1q4/5/t69e/TQQ/eqf/9e6t27m0aNGqFvv53ven/06BuVmpri+jrnnE5ur9PS9unJJ8crNTVFb745zaP//Pw89erVVYMHX+LxXkFBvnr37qbPP//0qLFNmvSExoy547ifb968L1x9r127WqmpKcdsO336VI0efeNx+ytXWlrqFtfo0Tdq+vSplTr3ZFdcXKwHH3xQKSkpSk1N1YwZMyo8Z+/everYsaNWrFjhhwgBAAAqZgl0ACe7CEukYkLq6HDpIe0vTFNUSItAhwQAqEGKiop0xx0369xzu+uVV6YpNDRMK1b8pCeeGKeQkBD17NlbEyc+o9LSUknSe++9o02bNuiJJya7+qhbN1aSZLFYtGTJYl177Q1uYyxbtkQ2m+2o40dEROrcc7tr0aIf9K9/DXJ7z2azafHiH3THHfdU+vO0a9dBc+fOr7hhJSxY8D+9/fYMV1wTJz4jiyXEK30Hu8mTJ2vjxo2aOXOm9u3bp/vvv18NGjRQ//79j3nO+PHjVVBQ4McoAQAAjo+ilB8kRyTr8KFDSi9MU7MYilIAgL+sXr1CBQUFGjPmAdexRo0aa+vWLfr888/Us2dvxcTUcb1ntVplsYQoLi7eo68OHTpq7drVysg4oPr1E1zHf/xxkdq2bafMzIyjxtCnzwUaN26s8vLyFBUV5Tq+Zs0qFRcXq3v3npX+PCEhR4+tOpxOp9vrv+ehNisoKNCcOXM0bdo0tW3bVm3bttXWrVs1e/bsYxalPv/8c+Xn5/s5UgAAgONj+Z4fJB7ZVyqtgM3OAQDuDMOkwsICbdz4i9vxm24arfvvf6hKfSUkJKpFi5ZaunSx61hJSYlWrvxJqannHfO8rl27KTw83O08Sfr++2/VrVt3RUREaMOGdbrlluvVu3c39emTqjFj7lBmZqZHX/9cvrdjx3bXeXfccbMOHTro1v6LLz7TVVddrp49z9FFF/XWc89Nkt1u19q1qzVx4gSlp6e5lij+c/nevHlfaNiwwerRo6uuv3641q1b63pv8OBL9Mknc3Tjjdfo/PPP1TXXXKXNm3+rdC5rss2bN8tms6ljx46uY507d9b69evlcDg82ufk5OiZZ57RY4895s8wAQAAKkRRyg+SI3gCHwD4itPplLOw0L9f/5jBcyJSUrqoceMmuuWW63TLLdfrzTenadOmjYqNjVViYlKV++vevYeWLPmruLRmzUqddlpT1asXd8xzQkNDdd55vbRw4feuYzabTT/+uEh9+/ZXXl6e7rvv3+rS5RzNmvWhnn9+ivbu3at33nnzuLGUlJTovvv+rQYNGmrGjNnq2bO35s79xPX+zz+v0X/+84xuuuk2vffeJxozZqy++mqulixZpHbtOuiOO+5RQkKi5s6dr4SERLe+5837Qi+8MFlXX32N3n77PaWkdNG9996pjIwDrjYzZkzV1Vdfo7feek9RUVF68cVnKp3HmiwjI0OxsbEKDQ11HYuPj1dxcbEOHjzo0f7pp5/WoEGD1KIFs7UBAEDNwvI9P0iylv2jIr2AJ/ABgDc5nU4duvUG2TZu8Ou4lnYdVOe/r8swjBPuKywsTK+88oZmzXpT33//raZPn6rp06fq9NNbasKEp9S48SlV6i81tYdmzXpThYWFslqtWrx4kbp371XheX379tcDD9ztOm/VqrLNsM85p5sOHTqokSNHaejQYTIMQw0aNFTPnufrt982HbfP1atX6tChQxozZqysVquaNDlVP/+8Rjk52ZIkqzVCDzzwiHr0OF+SlJzcQO+/P1s7dmxXjx7nKyoqSiaT6ajLAT/66H0NHjxUAwZcLIvFpFtuuV3r1q3Vxx9/qJtvHi1JGjDgEp13Xk9J0tChw/Tww/dXOo81WWFhoVtBSpLrdUlJidvxZcuWac2aNfryyy9PaEwvXOqopPJck3MEA65XBAuuVf+rbK4pSvlBkrV8plR6gCMBgJPQSXB3ERMTo9tuu1O33Xantm/fph9/XKT335+thx++XzNnvlelvlq0OF316sVp5cqf1L17Ty1dulivvjpd69f/fNzzOnVKUVRUlJYvX6pevfrohx8WqGfP82WxWBQXF68BAy7WBx/M1tatv2vnzh3atu13tWvX4bh97ty5XY0aNZbVanUda926jZYtWyJJatWqtcLCwjR9+lTt2PGH/vhjm/bu3aMuXc6p8HPu3LnTY0P3M85op127drheN2rU2PV9RETkMTd7DzZhYWEexafy1+Hh4a5jRUVFevTRRzVu3Di349URFxd9Quej6sg5ggnXK4IF12rNQ1HKD5IiyvaUSi/cJ6fT6ZW/rAMAJMMwVOe/r0tFRf4dODzcaz/LP//8U0VGRql3776SpKZNm6tp0+Zq2bK1xoy5Qzk5OYqNja1Sn6mpZUv46tWLU2xsrBo2bFRhUcpsNqtXr75atOgHde/eU4sXL9RTTz0rScrIOKBRo4arZcvWSkk5W//61yAtW7ZEmzb9ctw+y7gvdfz70/NWrPhJY8eOUf/+F+qcc87VtdfeqOeee7pSn/GfM4UkyW53yG7/a0+lkJCT80l9iYmJysnJkc1mk8VSdiuXkZGh8PBwxcTEuNpt2LBBe/bs0R133OF2/g033KCBAwdWaY+prKxceXHVKo7DMMr+0UTOEQy4XhEsuFb9rzznFaEo5QeJR5bvFdgKlFuaq5jQmArOAABUlmEY0t9m4gSb7du3acOGderVq7dMpr+2eoyKilZoaKgiIyOr3Gf37j00btyDqls3VuedV/HSvXJ9+lygMWNu16pVyxUREaEzz+wkSVq8+AdFR9fR5Mn/cbX96KMPKuzvtNOaac+e3W5P9du6dYvr/S+++FQXXfQv3XNP2bI6m82mP//cq86dz5Kk4xb+TjmliTZt2uj2ZMBNm35Rhw4dj3nOyaJ169ayWCxat26dUlLKNpVfs2aN2rVr53YNtW/fXt98843buf369dMTTzyhbt26VWlMp1PcxPsZOUcw4XpFsOBarXkoSvlBuDlcsaH1lFOSrfTCNIpSAFAL/fbbJo8lV2ee2UlDhlyp+fO/0kMP3asrrxyu+Pj62rFju6ZOnaJBg4YcdUZQRTp06Ci73a65cz/Rf//7eqXPO+OMdoqJqaPXX39FvXv3cxWFYmLqaP/+dK1evVLJyQ30ww8LtGjR92rVqs1x+zvrrLOVmJikp59+TKNG3aJff92o7777Vm3atHX1u3Hjev3xxzYZhqF33nlLWVmZbkvRcnMPa8+e3UpObuDW9//93zA9/fRjOvXU09S+fXvNnfuZ/vhjqx5+eEJVUhWUrFarBg4cqPHjx2vixIk6cOCAZsyYoaeeekpS2ayp6OhohYeHq0mTJh7nJyYmKi7u2BvfAwAA+AtFKT9JikguK0oV7NPpdVoGOhwAgJ+9+urLHsfef/9TNWrUWK+8Ml1vvPGaHnzwXuXl5SopKVkXX3yprrxyeLXGslgs6tq1m375Zb1atKja75zevftp1qw3NXbso65j55/fV+vX/6yHH75fhmGodes2Gj3635o+fapHoe2fcUye/B9NmvSErrvuajVr1lyXXTZEmzf/Kkm67rqbNHHieN100zWKjIxS167dNHDgYNdsqs6dz1LDho01cuRQvfLKG/+Is6+ys7P0xhuvKTs7S82bn67nn5+iJk1OrdLnDVZjx47V+PHjNXLkSEVFRen2229Xv379JEmpqal66qmndNlllwU4SgAAgOMznN58rnUQyMz0zRpSw5Di46OP2f/jPz+qH9IW6OZWo3VF06u8H0ANU1E+aiNy4omcuCMfnv6Zk9LSEmVlpSkuLlkhIVWfQXSysFhMstkcFTesJbyRj+NdW+XXYW3GzyX/4XcBggnXK4IF16r/Vfb+yVRhC3hFkrVss/O0wrQARwIAAAAAABB4FKX8JPnIE/j2F1CUAgAAAAAAoCjlJ4nMlAIAAAAAAHChKOUnyRFlTw3aX5imWraNFwAAAAAAgAeKUn6SEJ4oQ4aK7EU6WJIT6HAAAAAAAAACiqKUn4SaQxUXHi9JSi9MD3A0AAAAAAAAgUVRyo/Kn8CXXrAvwJEAAAAAAAAEFkUpP3IVpdjsHAAAAAAA1HIBLUoVFxfrwQcfVEpKilJTUzVjxoxjtv322281YMAAdezYUVdeeaU2bdrkx0i9IymivCjF8j0AAAAAAFC7BbQoNXnyZG3cuFEzZ87UuHHjNGXKFM2fP9+j3datW3XPPffopptu0ty5c9W6dWvddNNNKiwsDEDU1cfyPQCovQ4fPqyXX35BQ4b8S717d9OwYYP14YfvyuFwuNoMHnyJbrnleo+ntK5du1qpqSmu1wMHXlSpdn83ePAlSk1NOeZXdaSmpmjt2tXVOhcAAACwBGrggoICzZkzR9OmTVPbtm3Vtm1bbd26VbNnz1b//v3d2i5dulTNmzfXwIEDJUl33323Zs+erW3btqldu3YBiL56WL4HALXToUMHddNN1yo+vr4eeOARJSc30G+/bdILLzyjP//cq7vuus/V9pdf1uurrz7XxRdfetw+K9uu3LRpb8vhsEuSXnzxOUnSnXfeU81PVGbu3PmKialzQn0AAACg9grYTKnNmzfLZrOpY8eOrmOdO3fW+vXr3f5qLEl169bVtm3btGbNGjkcDn3yySeKiorSKaec4u+wT8jfl+85nI4KWgMAThavvTZFISEheu65l9W581lq0KChevfupwceeESffDJHu3fvcrVNTm6g1157WYcPHzpun5VtVy42NlZxcfGKi4tXWFiYwsLCXK/j4uKr9bni4uIVEhJSrXMBAACAgBWlMjIyFBsbq9DQUNex+Ph4FRcX6+DBg25tL7zwQvXs2VNXXXWVzjjjDE2ePFkvvfSS6tQJrr/OJoQnyiSTSh0lyinODnQ4AAA/KCkp0YIF3+jyy69QWFiY23vdunXXiy++qqSkZNexoUOvVmhomF599eXj9lvZdpU1b94XuuWW6zR27BhdcEEPffPN18rPz9PEiRN08cV91bPnObrqqsu1ePFC1zl/X743ePAl+uSTObrxxmt0/vnn6pprrtLmzb95JTYAAACcnAJWlCosLHQrSElyvS4pKXE7npOTo4yMDD366KP68MMPdemll2rs2LHKysqq8riG4buvivoPMVtU31pfUtkSPl/GUhO+fJ3vYPwiJ+SEfJx4Tv7J6XTKVmL369c/93I6nj//3KvCwgK1atX2KL+TDHXqlOL2+9BqterOO8foyy/nauPGDcfst7LtquKXXzbotNOaaurUt9SlS1e9+OJz2rNnl154YYpmzfpQHTp01KRJj6u0tPSo58+YMVVXX32N3nrrPUVFRenFF5/xSlz+dLzrEAAAAN4VsD2lwsLCPIpP5a/Dw8Pdjj/77LM6/fTTNWzYMEnS448/rgEDBujjjz/WjTfeWKVx4+KiTyDqE++/cUxj7S/cr3xLjuLjfRtLTeDrfAcjcuKJnLgjH57Kc1JUVKTsbJPMZkMWi0lOp1PfTv1Nmbvy/BpP/SZR6nNTGxmVqFYUFuZLkurUiZbFUvHfgkwmQ71799a8ed303HNP6623ZstsLjvv7+dXtt3RlMf9z/4Mw9B1141y/R7u1Kmzhg0brmbNmkuSrr56hL744jMdPpyjxMQkSZLZbHL1c9FFl+j888+XJF111XA9+OC9lfrM3nCi4zgchkwmk2JjIz3uQwAAAOAbAStKJSYmKicnRzabTRZLWRgZGRkKDw9XTEyMW9tNmzZp+PDhrtcmk0mtWrXSvn1Vf4pdVlauqvAH7kozjLJ/NFXUf1xIgiRp6/7tyozJ9X4gNURl81GbkBNP5MQd+fD0z5yUlpbI4XDIbnfKZnOUzVgKQK6cTslmc1SqKBUVVVZQO3jwkJKTK95P0OEo+2x33nmvhg+/Qh988J6aNz9dUtmYVW139PidR+0vNraeLJZQ1/F+/S7Ujz8u1Keffqxdu3Zqy5bNkqSSEpurjd3ucH3foEFj1/fh4VbZbLYKY/EGi8V0wuPY7U45HA7l5OQrJMR9Jlj5dQgAAADvClhRqnXr1rJYLFq3bp1SUsoeRb1mzRq1a9dOJpP7XzsTEhL0xx9/uB3bsWNHtZ6853TKp//Yq6j/8ifwpRWm1Yp/dPo638GInHgiJ+7Ih6fynPwzL4ZhqNeoVrKX+vfhEeYQU6UKUpLUoEEjRUVFacuW39S6tecSvgceuFuXX/5/Ouuss/9xXkMNH36t3nhjqsaMGXuc/ivXrjL+uaz+iSfG6ZdfNqh//ws1cOBgxcXF6+abrz3m+SfDpuf8/w8AAMB/AranlNVq1cCBAzV+/Hht2LBBCxYs0IwZMzRixAhJZbOmioqKJElXXHGFPvzwQ3322WfatWuXnn32We3bt0+DBg0KVPjVVl6USi9IC3AkAHByMAxDllCzX78qW5CSJIvFot69++njjz/02ItpyZLFWrJkseLj6x/13GHDRio+Pl7Tpr1y3DEq264q8vPz9O238/XYYxN1/fU3qUePXsrNPSRJVdpTCwAAADiWgBWlJGns2LFq27atRo4cqQkTJuj2229Xv379JEmpqamaN2+epLKn7z3yyCOaOnWqBg4cqLVr12rmzJmKi4sLZPjVkhRxpChVSFEKAGqL6667Ufn5+br77tH6+ec1+vPPvfryy8/05JPjNWTIlTrttKZHPS8kJER3332/0tKOv1y9su2qIjQ0TOHhVi1c+L3S0vZpxYqf9PzzZRuXH2ujcwAAAKAqArZ8TyqbLTVp0iRNmjTJ470tW7a4vR4yZIiGDBnir9B8pnym1IHC/bI77TIb5gBHBADwtbi4eL366nTNmPG6HnvsER06dEgNGzbUqFE3aeDAwcc9NyWli/r0uUALFvzPK+0qKyQkRI8++pimTPmPPvrofSUnN9TIkddp2rRX9fvvm9WkyaleGQcAAAC1l+GsZXPwMzN9t9F5fHx0hf3bnXb1n99Tdqdd7/f6VAnWRO8HUwNUNh+1CTnxRE7ckQ9P/8xJaWmJsrLSFBeXrJCQ0Io7OEl5Y2Pvk4k38nG8a6v8OqzN+LnkP/wuQDDhekWw4Fr1v8rePwV0+V5tZDbMrkIUS/gAAAAAAEBtRVEqANjsHAAAAAAA1HYUpQIg2dpAEjOlAAAAAABA7UVRKgASI5IkSWkF3ntKEgAAAAAAQDChKBUA5TOl9hemBzgSAAAAAACAwKAoFQDle0qlFTJTCgAAAAAA1E4UpQIgKaKsKJVRlCG7wxbgaAAAAAAAAPyPolQA1AuLU4gpVA6nXQeKDgQ6HAAAAAAAAL+jKBUAJsOkRGvZZuc8gQ8AAAAAANRGFKUCJKm8KFVAUQoAaoPDhw/r5Zdf0JAh/1Lv3t00bNhgffjhu3I4HK42gwdfoltuuV5Op9Pt3LVrVys1NcX1euDAiyrV7u9uvXWUJkx4+KjvffPN1+rfv5dKSkqOGX9a2j6lpqYoLa1sP8TU1BStXbv6qG2PF8fRfP/9AuXkZEuSpk+fqtGjb6z0uQAAAAheFKUCpPwJfMyUAoCT36FDB3XjjSO1ZctveuCBRzRr1oe67rob9fbbb+rFF591a/vLL+v11VefV9hnZduV69PnAv300xKVlpZ6vPf999+qZ8/zFRoaWun+5s6dr3btOlS6/bGkp6fp0UcfUFFRkSTpyiuHa+LEZ064XwAAANR8FKUCJDGC5XsAUFu89toUhYSE6LnnXlbnzmepQYOG6t27nx544BF98skc7d69y9U2ObmBXnvtZR0+fOi4fVa2XblevfqosLBQq1evcDuen5+nlSuXq2/f/lX6THFx8QoJCanSOUfzz9leERERiompc8L9AgAAoOajKBUgrplSLN8DgJNaSUmJFiz4RpdffoXCwsLc3uvWrbtefPFVJSUlu44NHXq1QkPD9OqrLx+338q2KxcbG6uUlLO1aNEPbsd//HGRYmLqqGPHzsrIOKCHH75P/fv3Uq9eXXXddcO0YcO6o/b39+V7+fl5GjfuQfXte56GDr1Mmzf/6tZ2w4Z1uuWW69W7dzf16ZOqMWPuUGZmpiRpyJB/uf47b94XHsv3Nm7coFtuuV59+qRqyJB/6bPPPnK99+ST4/Xyy8/roYfuV+/e3XTZZRdp/vyvKpUPAAAABB5FqQBJiij7BwgzpQDgxDidTpUWF/n165+ze47nzz/3qrCwQK1atfV4zzAMdeqU4rZszmq16s47x+jLL+dq48YNx+y3su3+rk+fflqyZJHsdrvr2PffL1Dv3n1lMpn02GOPyG53aOrUNzVjxmzVr5+g5557usJ+n3nmKe3evVNTpryuu+66V++/P9v1Xl5enu6779/q0uUczZr1oZ5/for27t2rd955U5I0bdpM13979+7r1u/OnTt0xx236MwzO2nGjHd03XU3asqU/7gV1j7++EO1atVab7/9gXr0OF/PPDNReXl5lcoHAAAAAssS6ABqq0RrWVEqsyhDJfYShZorv48HAKCM0+nU/BfGK2P7734dt37Tlup/1zgZhlFh27y8XElSVFRUpfvv0aOXunbtpmeffVrTp8864XZ/b//MM09p/fqf1alTivLy8rRq1XJdd92Ncjqd6t69p3r2PF8JCYmSpMsuu0L33ntnBZ8vTz/8sEAvvfSaWrZsJUm65ppRev75SZKk4uIijRw5SkOHDpNhGGrQoKF69jxfv/22SZJUt26s679hYeFufX/xxac6/fSWuumm2yRJp5xyqnbu3KF3331bPXr0kiQ1b366hg+/RjabQ6NG3aQ5c97Tjh1/eGW/KwAAAPgWRakAiQ2NVZgpTMWOYmUUHVDDyEaBDgkAglTFhaFAqlOnbH+k3NzDVTrv3/++V8OHX6GPP/5AzZuffsLtJCkiIlLnnpuqhQu/U6dOKfrxx4VKTm6gVq1aS5IGDRqsBQv+p40bN2jXrp3asmWz29MBj2bPnl2y2+1q0eKvsVu3buP6Pi4uXgMGXKwPPpitrVt/186dO7Rt2++VKhrt3LlTbdq4zzBr16695s792PW6UaPGru8jI8sKfzabrcK+AQAAEHgUpQLEMAwlRSRrV95OpRXsoygFANVgGIb63zVOtpJiv45rCQ2r1CwpSWrQoJGioqK0Zctvat3acwnfAw/crcsv/z+dddbZ/zivoYYPv1ZvvDFVY8aMPU7/lWtXrm/f/vrPf57RXXfdp++//1Z9+lwgSXI4HLrrrtuUm5ur3r37qlu381RaWqqHHrq3Up/z70saLZa/NkDPyDigUaOGq2XL1kpJOVv/+tcgLVu2RJs2/VJhn0d7GqDd7pDd/leh7GibrVdleSUAAAAChz2lAijJyr5SAHCiDMNQSFi4X78qW5CSJIvFot69++njjz9UaWmp23tLlizWkiWLFR9f/6jnDhs2UvHx8Zo27ZXjjlHZdpLUtWs3FRYWaO3a1VqzZpXrqXs7d27XunVr9Z//vKIRI67TueemKiurbDPy4xV5TjmliSwWi3777a/Nzbdu3eL6fvHiHxQdXUeTJ/9HV1xxpTp06Kh9+/50vX+8XJ5yShNt2rTR7dimTRt0yilNKvycAAAAqPkoSgUQRSkAqB2uu+5G5efn6+67R+vnn9fozz/36ssvP9OTT47XkCFX6rTTmh71vJCQEN199/1KS9t33P4r204qm3103nm9NGXKC2ratLkaNz5FkhQVFS2TyaTvvvuf0tPT9MMPCzRjxlRJZU8QPJbIyCj173+R/vOfZ7Rp00atXbtaM2a87no/JqaO9u9P1+rVK/Xnn3v1zjtvadGi7119hodbJUnbtv2ugoICt74HDRqirVt/19Sp/9Xu3bv09ddf6pNP5uiyy4ZU+DkBAABQ81GUCqCkiAaSpPQCilIAcDKLi4vXq69OV4MGDfXYY49o+PD/0wcfvKtRo27S6NH/Pu65KSldXEvsvNFOkvr2vUBbt/6uvn3/ap+QkKh77nlAs2e/reHDr9CsWW/pzjvHyGw2u818Opq77rpXZ5zRXnfddZuefHK8Lr/8/1zvnX9+X11wwQA9/PD9GjVqhNauXa3Ro/+tXbt2qKSkRHXr1tUFFwzQo4+O1ZdffubWb1JSkiZPfkErVizTyJFDNXPmdI0efZcuuuhflfqcAAAAqNkMZy3beCEzM1e++MSGIcXHR1ep/0Vp32vCzw+rTd0zNOXc1ys+IYhUJx8nO3LiiZy4Ix+e/pmT0tISZWWlKS4uWSEhtfeppRaLSTbb8Tcgr028kY/jXVvl12Ftxs8l/+F3AYIJ1yuCBdeq/1X2/omZUgGUXD5TiuV7AAAAAACglqEoFUCJR/aUyi7OUrHdv0+OAgAAAAAACCSKUgEUExKjCEuEJGk/s6UAAAAAAEAtQlEqgAzD+NsT+NIDHA0AAAAAAID/UJQKsPIlfGkFFT/GGwBQppY9owN+wDUFAADgfxSlAiw5oqwoxfI9AKiYyVT2a8tutwU4EpxsSkrK9nY0my0BjgQAAKD24M4rwP6aKUVRCgAqYjKZFRISrry8gzKbzTKM2vm3FYfDkN3OzJ5yJ5IPp9OpkpJi5eXlyGqNchU+AQAA4HsUpQIs2bWnFEUpAKiIYRiqU6eesrLSlZ29P9DhBIzJZJLD4Qh0GDWGN/JhtUYpJqaelyICAABAZVCUCrCkCIpSAFAVFkuIEhIayWYrDXQoAWEYUmxspHJy8sU2SN7Jh9lsYYYUAABAAFCUCrDyp+8dKjmoQluBrJaIAEcEADWfYRgKCQkNdBgBYRhSeHi4QkJKKUqJfAAAAAQz/iwYYFEh0YqyREuS0gvTAxwNAAAAAACAf1CUqgFcS/jY7BwAAAAAANQSFKVqgCQ2OwcAAAAAALUMRakaINm12fm+AEcCAAAAAADgHxSlaoDE8plSBewpBQAAAAAAageKUjVAsrWBJCmNmVIAAAAAAKCWoChVAyRFJEmS9rOnFAAAAAAAqCUoStUA5Rud55bmKq80L8DRAAAAAAAA+B5FqRrAaolQndC6kpgtBQAAAAAAageKUjVEkrVsCV9aAftKAQAAAACAkx9FqRoi6chm5+mFPIEPAAAAAACc/ChK1RBJEWX7SqWzfA8AAAAAANQCFKVqiPLNztNZvgcAAAAAAGoBilI1hKsoxfI9AAAAAABQC1CUqiGSXcv39snpdAY4GgAAAAAAAN+iKFVDJB6ZKVVgK1BuaW6AowEAAAAAAPAtilI1RJg5TPXC4iSVzZYCAAAAAAA4mVGUqkGSrEmSpPQCnsAHAACOrbi4WA8++KBSUlKUmpqqGTNmHLPt559/rgsuuEDt27fX0KFDtWHDBj9GCgAAcGwUpWqQJGsDSVJaIUUpAABwbJMnT9bGjRs1c+ZMjRs3TlOmTNH8+fM92q1evVoPPfSQbr31Vn311Vfq2LGjbrjhBuXn5wcgagAAAHcUpWqQJNdm5xSlAADA0RUUFGjOnDl66KGH1LZtW/Xt21ejRo3S7NmzPdpmZGTo1ltv1aWXXqrGjRvrtttu08GDB/XHH38EIHIAAAB3lkAHgL/8tXyPPaUAAMDRbd68WTabTR07dnQd69y5s1577TU5HA6ZTH/9zXHAgAGu74uKivTWW28pLi5OzZo182vMAAAAR0NRqgYpX76XXpge4EgAAEBNlZGRodjYWIWGhrqOxcfHq7i4WAcPHlS9evU8zvnpp5903XXXyel06tlnn1VkZKQ/QwYAADgqilI1SPnyvf2FaXI6nTIMI8ARAQCAmqawsNCtICXJ9bqkpOSo57Ro0UKffPKJfvjhBz3wwANq1KiRzjzzzEqPyS2J/5TnmpwjGHC9IlhwrfpfZXNNUaoGSQhPlCFDRfYiHSzJUWyY5186AQBA7RYWFuZRfCp/HR4eftRz4uPjFR8fr9atW2v9+vV6//33q1SUiouLrna8qB5yjmDC9YpgwbVa81CUqkFCzaGKC49XZlGG0gvTKUoBAAAPiYmJysnJkc1mk8VSdiuXkZGh8PBwxcTEuLXdsGGDzGaz2rZt6zrWrFmzKm90npWVK6fzxGNHxQyj7B9N5BzBgOsVwYJr1f/Kc14RilI1TLK1QVlRqmCfWtdtE+hwAABADdO6dWtZLBatW7dOKSkpkqQ1a9aoXbt2bpucS9JHH32kP//8U9OnT3cd27Rpk9q0qdo9htMpbuL9jJwjmHC9IlhwrdY8poqbwJ8Sy5/AV5gW4EgAAEBNZLVaNXDgQI0fP14bNmzQggULNGPGDI0YMUJS2aypoqIiSdL//d//afny5Zo5c6Z27typl156SRs2bNA111wTwE8AAABQhqJUDZMcceQJfAUUpQAAwNGNHTtWbdu21ciRIzVhwgTdfvvt6tevnyQpNTVV8+bNkyS1bdtWU6ZM0UcffaR//etfWrRokaZPn67ExMRAhg8AACCJ5Xs1TpK17Al8zJQCAADHYrVaNWnSJE2aNMnjvS1btri97tWrl3r16uWv0AAAACotoDOliouL9eCDDyolJUWpqamaMWPGMdtu2bJFV155pdq3b69LLrlEy5cv92Ok/pMUUVaUSqMoBQAAAAAATmIBLUpNnjxZGzdu1MyZMzVu3DhNmTJF8+fP92iXm5ur6667Ts2bN9cXX3yhvn37avTo0crKygpA1L5VPlNqf2G6HE5HgKMBAAAAAADwjYAVpQoKCjRnzhw99NBDatu2rfr27atRo0Zp9uzZHm0//fRTRUREaPz48WrSpInuuOMONWnSRBs3bgxA5L6VEJ4gk2FWqaNE2cUnX9ENAAAAAABACmBRavPmzbLZbOrYsaPrWOfOnbV+/Xo5HO4zhFauXKnevXvLbDa7jn388cfq0aOH3+L1F7PJovrh9SVJ6YXpAY4GAAAAAADANwJWlMrIyFBsbKxCQ0Ndx+Lj41VcXKyDBw+6td2zZ4/q1aunRx55RN26ddMVV1yhNWvW+Dli/3Et4eMJfAAAAAAA4CQVsKfvFRYWuhWkJLlel5SUuB0vKCjQ66+/rhEjRmjatGn66quvdP311+vrr79WcnJylcY1jBOLu6J+vdF/ckSy1mf/rPTCfT6L19e8mY+TBTnxRE7ckQ9P5MQTOXHnj3yQawAAAN8IWFEqLCzMo/hU/jo8PNztuNlsVuvWrXXHHXdIktq0aaOlS5dq7ty5uvnmm6s0blxc9AlE7Z/+m8adKu2VDjqzFB/v23h9zdf5DkbkxBM5cUc+PJETT+TEHfkAAAAIPgErSiUmJionJ0c2m00WS1kYGRkZCg8PV0xMjFvb+vXrq2nTpm7HTj31VKWlVX15W1ZWrpzO6sd9LIZRdkPsjf5jFCdJ2pG9S5mZuV6Izv+8mY+TBTnxRE7ckQ9P5MQTOXHnj3yUjwEAAADvClhRqnXr1rJYLFq3bp1SUlIkSWvWrFG7du1kMrlvdXXmmWdq1apVbse2b9+uiy++uMrjOp3y6U28N/pPtCZJktIL04L+Hxy+zncwIieeyIk78uGJnHgiJ+7IBwAAQPAJ2EbnVqtVAwcO1Pjx47VhwwYtWLBAM2bM0IgRIySVzZoqKiqSJA0dOlRbtmzRyy+/rF27dunFF1/Unj17dOmllwYqfJ9KtjaQJB0o3C+70x7gaAAAAAAAALwvYEUpSRo7dqzatm2rkSNHasKECbr99tvVr18/SVJqaqrmzZsnSWrYsKHeeOMN/fDDD7r44ov1ww8/6PXXX1diYmIgw/eZuPB4WQyLbE6bsooyAx0OAAAAAACA1wVs+Z5UNltq0qRJmjRpksd7W7ZscXvduXNnffLJJ/4KLaDMhlkJ1kTtK/hTaYX7lGA9OYtvAAAAAACg9groTCkcW5I1WZK0vyA9wJEAAAAAAAB4H0WpGiopoqwolVa4L8CRAAAAAAAAeB9FqRqqfKZUekFagCMBAAAAAADwPopSNVT5E/j2F7J8DwAAAAAAnHwoStVQiSzfAwAAAAAAJzGKUjVUkjVJkpRRlCG7wxbgaAAAAAAAALyLolQNVS8sTiGmUDmcdh0oOhDocAAAAAAAALyKolQNZTJMSjwyWyq9kM3OAQAAAADAyYWiVA2WzBP4AAAAAADASYqiVA2WVF6UYqYUAAAAAAA4yVS7KPXHH38oNzdXkvTjjz9qwoQJmjNnjtcCg5RU/gS+Ap7ABwDAyYD7JwAAgL9Uqyj1wQcf6F//+pd+++03/frrr7rlllu0Z88evfjii3rxxRe9HWOtVT5Tan9heoAjAQAAJ4r7JwAAAHfVKkq98cYbmjRpkrp06aKPP/5YrVu31htvvKEXXniBv/Z5UVJEA0lSWiEzpQAACHbcPwEAALirVlFq//796ty5syTphx9+UJ8+fSRJSUlJys/P9150tVzSkafvZRVlqsReEuBoAADAieD+CQAAwJ2lOic1bdpUX3zxherVq6d9+/apT58+Ki0t1YwZM9SqVStvx1hr1Q2NVbg5XEX2ImUUHVDDyEaBDgkAAFQT908AAADuqlWUuv/++/Xvf/9bhw4d0lVXXaVmzZrpscce07fffqvXXnvN2zHWWoZhKNGarF15O5RWsI+iFAAAQYz7JwAAAHfVKkp17dpVP/30k3Jzc1WnTh1J0q233qqxY8cqJCTEqwHWdslHilLphWmBDgUAAJwA7p8AAADcVWtPKUlasmSJbDabJOmjjz7Sgw8+qP/+978qKWHvI29KjCh7Ah9FKQAAgh/3TwAAAH+pVlHqv//9r+68807t3btXK1eu1KOPPqrk5GR9++23euqpp7wdY62WZD1SlCqgKAUAQDDj/gkAAMBdtYpSH374oV5++WV16NBBc+fO1VlnnaUJEybo6aef1rx587wdY62WXF6UKtwX4EgAAMCJ4P4JAADAXbWKUocOHVLTpk3ldDq1cOFC9erVS5IUFRUlu93u1QBruyTX8r30AEcCAABOBPdPAAAA7qq10XmrVq00ffp01a1bV9nZ2erbt6/279+v559/XmeeeaaXQ6zdkqwNJEnZxVkqthcrzBwW4IgAAEB1cP8EAADgrlozpcaPH6/Vq1dr5syZuvvuu9WwYUO98cYb+vPPPzVu3Dhvx1irRYdEK8ISIUnaz2bnAAAELe6fAAAA3FV7ptTcuXPdjt17770KDQ31SlD4i2EYSrI20PbcbUorSNMpUacGOiQAAFAN3D8BAAC4q1ZRSpJ+/fVXTZ8+Xdu3b5fdbtdpp52mYcOGqUuXLt6MD5KSrEnanrtN6cyUAgAgqHH/BAAA8JdqLd/79ttvdcUVV8jpdOqyyy7TZZddJsMwdN1112nBggXejrHWS4oo21eK5XsAAAQv7p8AAADcVWum1IsvvqgxY8bommuucTv+1ltv6eWXX1afPn28ERuOSLKWPYEvrYCiFAAAwYr7JwAAAHfVmim1Z88e12OM/65Xr17asWPHCQcFd8kRZUUplu8BABC8uH8CAABwV62iVLNmzbR48WKP44sWLVLDhg1POCi4K58pRVEKAIDgxf0TAACAu2ot37v99tt1++23a/369erQoYMkad26dfrf//6nyZMnezVASIlHilKHSg6q0FYgqyUiwBEBAICq4v4JAADAXbVmSvXq1UvTpk1TcXGx3nvvPX3yySdyOp169913deGFF3o7xlovKiRK0SHRkpgtBQBAsOL+CQAAwF21ZkpJUteuXdW1a1e3Y8XFxdqzZ48aN258woHBXaI1WbmluUovSNdp0c0CHQ4AAKgG7p8AAAD+Uq2ZUseycuVK9evXz5td4ohkawNJUlrhvgBHAgAAvIn7JwAAUFt5tSgF30mKSJIk7Wf5HgAAAAAAOAlQlAoSSeUzpQooSgEAAAAAgOBHUSpIJB15Ah8bnQMAAAAAgJNBpTc6X7VqVYVttmzZckLB4NiSIsqKUizfAwAgeHD/BAAAcGyVLkoNHz68Uu0Mw6h2MDi2JGvZnlK5pbnKK81TVEhUgCMCAAAV4f4JAADg2CpdlNq8ebMv40AFrJYI1Q2tq4MlB7W/ME1RIS0CHRIAAKgA908AAADHxp5SQSTxyL5SaQX7AhwJAAAAAADAiaEoFUSSI8qewJdemB7gSAAAAAAAAE4MRakgUr6vVHohM6UAAAAAAEBwoygVRMqX76UX8AQ+AAAAAAAQ3ChKBZG/lu9RlAIAAAAAAMGNolQQ+Wv5XpqcTmeAowEAAAAAAKg+ilJBpHz5XoGtQIdLDwc4GgAAAAAAgOqjKBVEwsxhqhcWJ0nazxI+AAAAAAAQxChKBZmkI7Ol0gp4Ah8AAAAAAAheFKWCTHlRKr0wPcCRAAAAAAAAVB9FqSCTHFFelGL5HgAAAAAACF4UpYJM+Wbn6SzfAwAAAAAAQYyiVJBJjmggieV7AAAAAAAguFGUCjLle0rtL0yT0+kMcDQAAAAAAADVQ1EqyCRYE2XIUJG9SAdLcgIdDgAAAAAAQLVQlAoyIaYQxYfXl8Rm5wAAAAAAIHhRlApCSa7NzilKAQAAAACA4ERRKgglRZQVpdIKeQIfAAAAAAAIThSlgpBrs/MCnsAHAAAAAACCE0WpIFRelGKmFAAAtVNxcbEefPBBpaSkKDU1VTNmzDhm24ULF+rSSy9Vx44ddckll+i7777zY6QAAADHRlEqCJUv30svZKYUAAC10eTJk7Vx40bNnDlT48aN05QpUzR//nyPdps3b9bo0aN1+eWX67PPPtPQoUN15513avPmzQGIGgAAwJ0l0AGg6pKtDSRJ+wvT5XA6ZDKoLQIAUFsUFBRozpw5mjZtmtq2bau2bdtq69atmj17tvr37+/W9ssvv9Q555yjESNGSJKaNGmi77//Xl9//bVatWoViPABAABcAlrNqMrU83J79+5Vx44dtWLFCj9EWDPVD68vk2FWqaNE2cVZgQ4HAAD40ebNm2Wz2dSxY0fXsc6dO2v9+vVyOBxubQcNGqQxY8Z49JGbm+vzOAEAACoS0JlSf596vm/fPt1///1q0KCBx1/5/m78+PEqKCjwY5Q1j9lkUUJ4gtIL05RemK748PqBDgkAAPhJRkaGYmNjFRoa6joWHx+v4uJiHTx4UPXq1XMdb9asmdu5W7du1U8//aShQ4dWaUzDOLGYUXnluSbnCAZcrwgWXKv+V9lcB6woVZWp5+U+//xz5efn+znSminJmlxWlCrYpzNi2wU6HAAA4CeFhYVuBSlJrtclJSXHPC87O1u33367OnXqpN69e1dpzLi46KoHihNCzhFMuF4RLLhWa56AFaWONfX8tddek8PhkMnkvrIwJydHzzzzjGbMmKGLL77Y3+HWOEkRyVK2lF6YFuhQAACAH4WFhXkUn8pfh4eHH/WczMxMXXvttXI6nXrppZc87rMqkpWVK6ezevGiagyj7B9N5BzBgOsVwYJr1f/Kc16RgBWlqjL1XJKefvppDRo0SC1atPB3qDVSkvXIE/gKKEoBAFCbJCYmKicnRzabTRZL2a1cRkaGwsPDFRMT49F+//79ro3O3377bY97rMpwOsVNvJ+RcwQTrlcEC67VmidgRamqTD1ftmyZ1qxZoy+//PKEx/XVGlJ/r1FNjjhSlCpMq5HrYlmz64mceCIn7siHJ3LiiZy480c+alquW7duLYvFonXr1iklJUWStGbNGrVr185jBlRBQYFGjRolk8mkt99+W/Xrsw8lAACoOQJWlKrs1POioiI9+uijGjdu3DGnpFeFL9aQOp1O/bDlgNqFhqq+n9aotrQ3k9ZLB4rTFR9fc9fFsmbXEznxRE7ckQ9P5MQTOXFXm/JhtVo1cOBAjR8/XhMnTtSBAwc0Y8YMPfXUU5LKZk1FR0crPDxcU6dO1e7duzVr1izXe1LZvVZ0dO3JGQAAqJkCVpSq7NTzDRs2aM+ePbrjjjvczr/hhhs0cOBAPfbYY1Ua1xdrSFfvPqibP9ygbs3j9NKgtn6ZDmgtqSNJSstP0/6MgzIbZt8PWgWs2fVETjyRE3fkwxM58URO3PkjH5XdE8Gfxo4dq/Hjx2vkyJGKiorS7bffrn79+kmSUlNT9dRTT+myyy7T//73PxUVFWnIkCFu5w8aNEhPP/10IEIHAABwCVhRqrJTz9u3b69vvvnG7dx+/frpiSeeULdu3ao8ri/WkDaqa5Uhaem2LO3NKVSDOlbvDnAU9cLiZTEssjltyijMUKI1yedjVgdrdj2RE0/kxB358EROPJETd7UtH1arVZMmTdKkSZM83tuyZYvr+/nz5/szLAAAgCqp2qNXvOjvU883bNigBQsWaMaMGa6NODMyMlRUVKTw8HA1adLE7Usqm2kVFxcXqPDdJEaHqUuTupKkr3494JcxzYZZCdZESTyBDwAAAAAABJ+AFaWksqnnbdu21ciRIzVhwgSPqefz5s0LZHhVcnHbsgLRV5v2y+mnP9UmWxtIkvYXpPtlPAAAAAAAAG8J2PI9qfJTz6vyXqD0ahGvqLA/9OehIq3787A6Nqrj8zETI5KkLCmtcJ/PxwIAAAAAAPCmgM6UOpmEh5h1YbuyfZ2+2rTfL2OWz5RKL2D5HgAAAAAACC4Upbzo8k6NJEkLfs9QUand5+MlWZMlsacUAAAAAAAIPhSlvOisU+upYZ1w5ZfYtXBbls/HS4qgKAUAAAAAAIITRSkvMpkMXdgmQZJ/lvCVz5TKKDwgm8Pm8/EAAAAAAAC8haKUl1105Cl8K3fn6EBusU/HqhcWp1BTqBxyKKPogE/HAgAAAAAA8CaKUl7WqK5VZzaMkcMpff2bbwtFhmEo0Vq2uXpaAU/gAwAAAAAAwYOilA9c1KZsttRXm/bL6XT6dKzkiLIn8O0vTPfpOAAAAAAAAN5EUcoH+rSsrzCLSTuyC/Tr/jyfjpV4ZF+ptEJmSgEAAAAAgOBBUcoHosIs6tk8TpLvNzxPOrJ8L72AJ/ABAAAAAIDgQVHKR8o3PP9m8wGV2Bw+G4flewAAAAAAIBhRlPKRLqfEqn5UqA4V2bRkR7bPxmH5HgAAAAAACEYUpXzEbDI0oHWCJN8u4Us+UpTKKspUib3EZ+MAAAAAAAB4E0UpHypfwrd0R7ZyCnxTMKoTWlfh5nA55dSBIt/uXwUAAAAAAOAtFKV8qGlcpFonRsnucGr+5gyfjGEYhpKOzJZis3MAAAAAABAsKEr52MVHZkv5cgmfqyjFvlIAAAAAACBIUJTysX6tEmQxGdpyIE/bMvJ9MkbSkSfwpfMEPgAAAAAAECQoSvlYXWuIUpvWkyR96aPZUknWJElSWgEzpQAAAAAAQHCgKOUH5Uv4vv5tv2wOp9f7L58ptb+QPaUAAAAAAEBwoCjlB+eeVk91rSHKLijVip05Xu+/fE+pNDY6BwAAAAAAQYKilB+EmE26oFV9Sb5ZwpccUVaUyinJVrG92Ov9AwAAAAAAeBtFKT8pX8K3+I9MHS4q9WrfUZZoRVoiJbGEDwAAAAAABAeKUn7SMiFKzeIjVGJ3asGWDK/2bRiGElnCBwAAAAAAgghFKT8xDEMXtSmbLfXlpgNe7798CV86M6UAAAAAAEAQoCj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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:436: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:436: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:436: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:436: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:436: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:436: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:436: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_31188\\3708078790.py:436: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型性能对比表:\n",
      "     模型  测试集准确率     测试集损失    训练时间(秒)\n",
      "0   MLP  0.9798  0.082813  26.712217\n",
      "1  LSTM  0.9877  0.051115  52.474212\n",
      "2   CNN  0.9927  0.026385  29.910241\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x600 with 6 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "f30e8cb1a32717ac"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "249689972fd9f714"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
